function [hdr] = ft_read_header(filename, varargin)

% FT_READ_HEADER reads header information from a variety of EEG, MEG and other time
% series data files and represents the header information in a common
% data-independent structure. The supported formats are listed below.
%
% Use as
%   hdr = ft_read_header(filename, ...)
%
% Additional options should be specified in key-value pairs and can be
%   'headerformat'   = string
%   'fallback'       = can be empty or 'biosig' (default = [])
%   'checkmaxfilter' = boolean, whether to check that maxfilter has been correctly applied (default = true)
%   'chanindx'       = list with channel indices in case of different sampling frequencies (only for EDF)
%   'chantype'       = string or cell-array with strings, channel types to be read (only for NeuroOmega and BlackRock)
%   'coordsys'       = string, 'head' or 'dewar' (default = 'head')
%   'headerformat'   = name of a MATLAB function that takes the filename as input (default is automatic)
%   'password'       = password structure for encrypted data set (for dhn_med10, mayo_mef30, mayo_mef21)
%   'readbids'       = string, 'yes', no', or 'ifmakessense', whether to read information from the BIDS sidecar files (default = 'ifmakessense')
%   'splitlabel'     = string, 'yes' or 'no', split the channel labels on the '-' and return the first part (default = 'yes' for CTF and FieldLine, 'no' for others)
%
% This returns a header structure with the following fields
%   hdr.Fs          = sampling frequency
%   hdr.nChans      = number of channels
%   hdr.nSamples    = number of samples per trial
%   hdr.nSamplesPre = number of pre-trigger samples in each trial
%   hdr.nTrials     = number of trials
%   hdr.label       = Nx1 cell-array with the label of each channel
%   hdr.chantype    = Nx1 cell-array with the channel type, see FT_CHANTYPE
%   hdr.chanunit    = Nx1 cell-array with the physical units, see FT_CHANUNIT
%
% For continuously recorded data, this will return nSamplesPre=0 and nTrials=1.
%
% For some data formats that are recorded on animal electrophysiology
% systems (e.g. Neuralynx, Plexon), the following optional fields are
% returned, which allows for relating the timing of spike and LFP data
%   hdr.FirstTimeStamp      number, represented as 32-bit or 64-bit unsigned integer
%   hdr.TimeStampPerSample  number, represented in double precision
%
% Depending on the file format, additional header information can be
% returned in the hdr.orig subfield.
%
% To use an external reading function, you can specify an external function as the
% 'headerformat' option. This function should take the filename as input argument.
% Please check the code of this function for details, and search for BIDS_TSV as
% example.
%
% The following MEG dataformats are supported
%   CTF (*.ds, *.res4, *.meg4)
%   Neuromag/Elekta/Megin (*.fif)
%   BTi/4D (*.m4d, *.pdf, *.xyz)
%   Yokogawa/Ricoh (*.ave, *.con, *.raw)
%   NetMEG (*.nc)
%   ITAB - Chieti (*.mhd)
%   Tristan Babysquid (*.fif)
%   York Instruments (*.meghdf5)
%
% The following EEG dataformats are supported
%   ANT - Advanced Neuro Technology, EEProbe (*.avr, *.eeg, *.cnt)
%   BCI2000 (*.dat)
%   Biosemi (*.bdf)
%   Bitalino OpenSignals (*.txt)
%   BrainVision (*.eeg, *.seg, *.dat, *.vhdr, *.vmrk)
%   CED - Cambridge Electronic Design (*.smr)
%   EGI - Electrical Geodesics, Inc. (*.egis, *.ave, *.gave, *.ses, *.raw, *.sbin, *.mff)
%   GTec (*.mat, *.hdf5)
%   GTec Unicorn (*.csv)
%   Generic data formats (*.edf, *.gdf)
%   Megis/BESA (*.avr, *.swf, *.besa)
%   Mega Neurone (directory)
%   Natus/Nicolet/Nervus (.e files)
%   Nihon Kohden (*.m00, *.EEG)
%   NeuroScan (*.eeg, *.cnt, *.avg)
%   Nexstim (*.nxe)
%   OpenBCI (*.txt)
%   PhysioNet (*.hea, *.dat)
%   TMSi (*.Poly5)
%
% The following spike and LFP dataformats are supported
%   CED - Cambridge Electronic Design (*.smr)
%   MPI - Max Planck Institute (*.dap)
%   Neuralynx (*.ncs, *.nse, *.nts, *.nev, *.nrd, *.dma, *.log)
%   Neurodata Without Borders (*.nwb)
%   Neurosim  (neurosim_spikes, neurosim_signals, neurosim_ds)
%   NeuroOmega (*.mat transformed from *.mpx)
%   Neuropixel data recorded with SpikeGLX (*.bin, *.meta)
%   Plextor (*.nex, *.plx, *.ddt)
%   Windaq (*.wdq)
%
% The following NIRS dataformats are supported
%   Artinis - Artinis Medical Systems B.V. (*.oxy3, *.oxy4, *.oxy5, *.oxyproj)
%   BUCN - Birkbeck college, London (*.txt)
%   SNIRF - Society for functional near-infrared spectroscopy (*.snirf)
%
% The following Eyetracker dataformats are supported
%   EyeLink - SR Research (*.asc)
%   SensoMotoric Instruments - (*.txt)
%   Tobii - (*.tsv)
%
% See also FT_READ_DATA, FT_READ_EVENT, FT_WRITE_DATA, FT_WRITE_EVENT,
% FT_CHANTYPE, FT_CHANUNIT

% Copyright (C) 2003-2024 Robert Oostenveld
%
% This file is part of FieldTrip, see http://www.fieldtriptoolbox.org
% for the documentation and details.
%
%    FieldTrip is free software: you can redistribute it and/or modify
%    it under the terms of the GNU General Public License as published by
%    the Free Software Foundation, either version 3 of the License, or
%    (at your option) any later version.
%
%    FieldTrip is distributed in the hope that it will be useful,
%    but WITHOUT ANY WARRANTY; without even the implied warranty of
%    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
%    GNU General Public License for more details.
%
%    You should have received a copy of the GNU General Public License
%    along with FieldTrip. If not, see <http://www.gnu.org/licenses/>.
%
% $Id$

% TODO channel renaming should be made a general option (see bham_bdf)

persistent cacheheader        % for caching the full header
persistent cachechunk         % for caching the res4 chunk when doing realtime analysis on the CTF scanner
persistent db_blob            % for fcdc_mysql
global FIFF                   % for fiff-files

if isempty(db_blob)
  db_blob = false;
end

if iscell(filename)
  % use recursion to read the header from multiple files
  ft_warning('concatenating header from %d files', numel(filename));

  hdr = cell(size(filename));
  for i=1:numel(filename)
    hdr{i} = ft_read_header(filename{i}, varargin{:});
  end

  allhdr = cat(1, hdr{:});
  if numel(unique([allhdr.label]))==sum([allhdr.nChans])
    % each file has different channels, concatenate along the channel dimension
    for i=1:numel(filename)
      assert(isequal(hdr{i}.Fs, hdr{1}.Fs), 'sampling rates are not consistent over files');
      assert(isequal(hdr{i}.nSamples, hdr{1}.nSamples), 'number of samples is not consistent over files');
      assert(isequal(hdr{i}.nTrials, hdr{1}.nTrials), 'number of trials is not consistent over files');
    end
    combined          = hdr{1}; % copy the first header as the general one
    combined.label    = [allhdr.label];
    combined.chanunit = [allhdr.chanunit];
    combined.chantype = [allhdr.chantype];
    combined.nChans   = sum([allhdr.nChans]);
    combined.orig     = hdr;    % store the original header details of all files
  else
    % each file has the same channels, concatenate along the time dimension
    ntrl = nan(size(filename));
    nsmp = nan(size(filename));
    for i=1:numel(filename)
      assert(isequal(hdr{i}.Fs, hdr{1}.Fs), 'sampling rates are not consistent over files');
      assert(isequal(hdr{i}.label, hdr{1}.label), 'channels are not consistent over files');
      ntrl(i) = hdr{i}.nTrials;
      nsmp(i) = hdr{i}.nSamples;
    end
    % the subsequent code concatenates the files over time, i.e. each file has the same channels
    combined      = hdr{1}; % copy the first header as the general one
    combined.orig = hdr;    % store the original header details of all files
    if all(ntrl==1)
      % each file is a continuous recording
      combined.nTrials  = ntrl(1);
      combined.nSamples = sum(nsmp);
    elseif all(nsmp==nsmp(1))
      % each file holds segments of the same length
      combined.nTrials  = sum(ntrl);
      combined.nSamples = nsmp(1);
    else
      ft_error('cannot concatenate files');
    end
  end % concatenate over channels or over time
  % return the header of the concatenated datafiles
  hdr = combined;
  return
end

% get the options
headerformat   = ft_getopt(varargin, 'headerformat');
retry          = ft_getopt(varargin, 'retry', false);     % the default is not to retry reading the header
chanindx       = ft_getopt(varargin, 'chanindx');         % this is used for EDF with different sampling rates
coordsys       = ft_getopt(varargin, 'coordsys', 'head'); % this is used for ctf and neuromag_mne, it can be head or dewar
coilaccuracy   = ft_getopt(varargin, 'coilaccuracy');     % empty, or a number between 0-2
coildeffile    = ft_getopt(varargin, 'coildeffile');      % empty, or a filename
chantype       = ft_getopt(varargin, 'chantype', {});
password       = ft_getopt(varargin, 'password', struct([]));
readbids       = ft_getopt(varargin, 'readbids', 'ifmakessense');
splitlabel     = ft_getopt(varargin, 'splitlabel');       % default for CTF and FieldLine is dealt with below

% this should be a cell array
if ~iscell(chantype); chantype = {chantype}; end

% optionally get the data from the URL and make a temporary local copy
filename = fetch_url(filename);

if isempty(headerformat)
  % only do the autodetection if the format was not specified
  headerformat = ft_filetype(filename);
end

if iscell(headerformat)
  % this happens for datasets specified as cell-array for concatenation
  headerformat = headerformat{1};
end

if strcmp(headerformat, 'compressed')
  % the file is compressed, unzip on the fly
  inflated     = true;
  filename     = inflate_file(filename);
  headerformat = ft_filetype(filename);
else
  inflated     = false;
end

% for backward compatibility with https://github.com/fieldtrip/fieldtrip/issues/1585
if islogical(readbids)
  % it should be either yes/no/ifmakessense
  if readbids
    readbids = 'yes';
  else
    readbids = 'no';
  end
end

realtime = any(strcmp(headerformat, {'fcdc_buffer', 'ctf_shm', 'fcdc_mysql'}));

% The checkUniqueLabels flag is used for the realtime buffer in case
% it contains fMRI data. It prevents 1000000 voxel names to be checked
% for uniqueness. fMRI users will probably never use channel names
% for anything.

if realtime
  % skip the rest of the initial checks to increase the speed for realtime operation
  checkUniqueLabels = false;
  % the cache and fallback option should always be false for realtime processing
  cache    = false;
  fallback = false;

else
  % check whether the file or directory exists
  if  ~exist(filename, 'file')
    ft_error('file or directory ''%s'' does not exist', filename);
  end
  % checking labels can be slow, especially if there are many of them (as for fMRI data)
  checkUniqueLabels = true;
  % get the rest of the options, this is skipped for realtime operation
  cache          = ft_getopt(varargin, 'cache');
  fallback       = ft_getopt(varargin, 'fallback');
  checkmaxfilter = ft_getopt(varargin, 'checkmaxfilter', true);

  if isempty(cache)
    if any(strcmp(headerformat, {'bci2000_dat', 'eyelink_asc', 'gtec_mat', 'gtec_hdf5', 'mega_neurone', 'nihonkohden_m00', 'smi_txt', 'biosig'}))
      % for these files the data and event information will be cached in hdr.orig
      % so that subsequent reading of data and events is fast
      cache = true;
    else
      cache = false;
    end
  end

  % ensure that the headerfile and datafile are defined, which are sometimes different than the name of the dataset
  [filename, headerfile, datafile] = dataset2files(filename, headerformat);
  if ~strcmp(filename, headerfile) && ~ft_filetype(filename, 'ctf_ds') && ~ft_filetype(filename, 'fcdc_buffer_offline') && ~ft_filetype(filename, 'fcdc_matbin')
    filename     = headerfile;                % this function should read the headerfile, not the dataset
    headerformat = ft_filetype(filename);     % update the filetype
  end
end % if skip initial check

% implement the caching in a data-format independent way
if cache && exist(headerfile, 'file') && ~isempty(cacheheader)
  % try to get the header from cache
  details = dir(headerfile);
  if isequal(details, cacheheader.details)
    % the header file has not been updated, fetch it from the cache
    % fprintf('got header from cache\n');
    hdr = rmfield(cacheheader, 'details');

    switch ft_filetype(datafile)
      case {'ctf_ds' 'ctf_meg4' 'ctf_old' 'read_ctf_res4'}
        % for realtime analysis end-of-file-chasing the res4 does not correctly
        % estimate the number of samples, so we compute it on the fly
        sz = 0;
        files = dir([filename filesep '*.*meg4']);
        for j=1:numel(files)
          sz = sz + files(j).bytes;
        end
        hdr.nTrials = floor((sz - 8) / (hdr.nChans*4) / hdr.nSamples);
    end

    return
  end % if the details correspond
end % if cache

% the support for head/dewar coordinates is still limited
if strcmp(coordsys, 'dewar') && ~any(strcmp(headerformat, {'fcdc_buffer', 'ctf_ds', 'ctf_old', 'ctf_meg4', 'ctf_res4', 'neuromag_fif', 'neuromag_mne'}))
  ft_error('dewar coordinates are not supported for %s', headerformat);
end

% deal with data that is organized according to BIDS
if strcmp(readbids, 'yes') || strcmp(readbids, 'ifmakessense')
  [p, f, x] = fileparts(filename);
  % check whether it is a BIDS dataset with json and tsv sidecar files
  % data in a BIDS tsv file (like physio and stim) will be explicitly dealt with in BIDS_TSV
  isbids = startsWith(f, 'sub-') && ~strcmp(x, '.tsv');
  if isbids
    % try to read the metadata from the BIDS sidecar files
    sidecar = bids_sidecar(filename);
    if ~isempty(sidecar)
      data_json = ft_read_json(sidecar);
    end
    sidecar = bids_sidecar(filename, 'channels');
    if ~isempty(sidecar)
      channels_tsv = ft_read_tsv(sidecar);
    end
    sidecar = bids_sidecar(filename, 'electrodes');
    if ~isempty(sidecar)
      electrodes_tsv = ft_read_tsv(sidecar);
    end
    sidecar = bids_sidecar(filename, 'optodes');
    if ~isempty(sidecar)
      optodes_tsv = ft_read_tsv(sidecar);
    end
    sidecar = bids_sidecar(filename, 'coordsystem');
    if ~isempty(sidecar)
      coordsystem_json = ft_read_json(sidecar);
    end
  end
end

% start with an empty header
hdr = [];

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% read the data with the low-level reading function
% please maintain this list in alphabetical order
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
switch headerformat

  case '4d'
    orig            = read_4d_hdr(datafile);
    hdr.Fs          = orig.header_data.SampleFrequency;
    hdr.nChans      = orig.header_data.TotalChannels;
    hdr.nSamples    = orig.header_data.SlicesPerEpoch;
    hdr.nSamplesPre = round(orig.header_data.FirstLatency*orig.header_data.SampleFrequency);
    hdr.nTrials     = orig.header_data.TotalEpochs;
    %hdr.label       = {orig.channel_data(:).chan_label}';
    hdr.label       = orig.Channel;
    [hdr.grad, elec] = bti2grad(orig);
    if ~isempty(elec)
      hdr.elec = elec;
    end

    % remember original header details
    hdr.orig        = orig;

  case {'4d_pdf', '4d_m4d', '4d_xyz'}
    orig            = read_bti_m4d(filename);
    hdr.Fs          = orig.SampleFrequency;
    hdr.nChans      = orig.TotalChannels;
    hdr.nSamples    = orig.SlicesPerEpoch;
    hdr.nSamplesPre = round(orig.FirstLatency*orig.SampleFrequency);
    hdr.nTrials     = orig.TotalEpochs;
    hdr.label       = orig.ChannelOrder(:);
    [hdr.grad, elec] = bti2grad(orig);
    if ~isempty(elec)
      hdr.elec = elec;
    end

    % remember original header details
    hdr.orig        = orig;

  case 'AnyWave'
    orig = read_ahdf5_hdr(datafile);
    hdr.orig = orig;
    hdr.Fs = orig.channels(1).samplingRate;
    hdr.nChans = numel(orig.channels);
    hdr.nSamples = orig.numberOfSamples;
    hdr.nTrials = orig.numberOfBlocks;
    hdr.nSamplesPre = 0;
    hdr.label = orig.label;
    hdr.reference = orig.reference(:);
    hdr.chanunit = orig.unit(:);
    hdr.chantype = orig.type(:);

  case 'bci2000_dat'
    % this requires the load_bcidat mex file to be present on the path
    ft_hastoolbox('BCI2000', 1);
    % this is inefficient, since it reads the complete data
    [signal, states, parameters, total_samples] = load_bcidat(filename);
    % convert into a FieldTrip-like header
    hdr             = [];
    hdr.nChans      = size(signal,2);
    hdr.nSamples    = total_samples;
    hdr.nSamplesPre = 0;  % it is continuous
    hdr.nTrials     = 1;  % it is continuous
    hdr.Fs          = parameters.SamplingRate.NumericValue;
    % there are some differences in the fields that are present in the
    % *.dat files, probably due to different BCI2000 versions
    if isfield(parameters, 'ChannelNames') && isfield(parameters.ChannelNames, 'Value') && ~isempty(parameters.ChannelNames.Value)
      hdr.label       = parameters.ChannelNames.Value;
    elseif isfield(parameters, 'ChannelNames') && isfield(parameters.ChannelNames, 'Values') && ~isempty(parameters.ChannelNames.Values)
      hdr.label       = parameters.ChannelNames.Values;
    else
      % give this warning only once
      ft_warning('creating fake channel names');
      for i=1:hdr.nChans
        hdr.label{i} = sprintf('%d', i);
      end
    end

    % remember the original header details
    hdr.orig.parameters       = parameters;
    % also remember the complete data upon request
    if cache
      hdr.orig.signal         = signal;
      hdr.orig.states         = states;
      hdr.orig.total_samples  = total_samples;
    end

  case 'besa_besa'
    if isempty(chanindx)
      hdr = read_besa_besa(filename);
    else
      % this will keep the non-selected channels hidden from the user
      hdr = read_besa_besa(filename, [], chanindx);
    end

  case 'besa_avr'
    orig = read_besa_avr(filename);
    hdr.Fs          = 1000/orig.di;
    hdr.nChans      = size(orig.data,1);
    hdr.nSamples    = size(orig.data,2);
    hdr.nSamplesPre = -(hdr.Fs * orig.tsb/1000);   % convert from ms to samples
    hdr.nTrials     = 1;
    if isfield(orig, 'label') && iscell(orig.label)
      hdr.label = orig.label;
    elseif isfield(orig, 'label') && ischar(orig.label)
      hdr.label = tokenize(orig.label, ' ');
    else
      % give this warning only once
      ft_warning('creating fake channel names');
      for i=1:hdr.nChans
        hdr.label{i} = sprintf('%d', i);
      end
    end

  case 'besa_swf'
    orig = read_besa_swf(filename);
    hdr.Fs          = 1000/orig.di;
    hdr.nChans      = size(orig.data,1);
    hdr.nSamples    = size(orig.data,2);
    hdr.nSamplesPre = -(hdr.Fs * orig.tsb/1000);   % convert from ms to samples
    hdr.nTrials     = 1;
    hdr.label       = orig.label;

  case 'biosig'
    % this requires the biosig toolbox
    ft_hastoolbox('BIOSIG', 1);
    hdr = read_biosig_header(filename);

  case {'biosemi_v3'}
    [dat, hdr.orig] = read_bdf(filename, 'Channels', {}, 'Verbose', false);
    hdr.Fs          = hdr.orig.sample_rate;
    hdr.nChans      = hdr.orig.num_channels;
    hdr.label       = {hdr.orig.channels.labels};
    % it is continuous
    hdr.nSamples    = hdr.orig.num_data_records * hdr.orig.record_duration * hdr.orig.sample_rate;
    hdr.nSamplesPre = 0;
    hdr.nTrials     = 1;

  case {'biosemi_v2', 'biosemi_bdf'}
    hdr = read_biosemi_bdf(filename);
    if any(diff(hdr.orig.SampleRate))
      ft_error('channels with different sampling rate not supported');
    end

    if ~ft_senstype(hdr, 'ext1020')
      % assign the channel type and units for the known channels
      hdr.chantype = repmat({'unknown'}, size(hdr.label));
      hdr.chanunit = repmat({'unknown'}, size(hdr.label));
      chan = ~cellfun(@isempty, regexp(hdr.label, '^[A-D]\d*$'));
      hdr.chantype(chan) = {'eeg'};
      hdr.chanunit(chan) = {'uV'};
    end

  case {'biosemi_v1', 'biosemi_old'}
    % this uses the openbdf and readbdf functions that were copied from EEGLAB
    orig = openbdf(filename);
    if any(orig.Head.SampleRate~=orig.Head.SampleRate(1))
      ft_error('channels with different sampling rate not supported');
    end
    hdr.Fs          = orig.Head.SampleRate(1);
    hdr.nChans      = orig.Head.NS;
    hdr.label       = cellstr(orig.Head.Label);
    % it is continuous data, therefore append all records in one trial
    hdr.nSamples    = orig.Head.NRec * orig.Head.Dur * orig.Head.SampleRate(1);
    hdr.nSamplesPre = 0;
    hdr.nTrials     = 1;
    hdr.orig        = orig;
    % close the file between separate read operations
    fclose(orig.Head.FILE.FID);

  case 'blackrock_nev'
    % read header for nsX file associated with NEV file
    % use ft_read_event to read event information in .nev file

    ft_hastoolbox('NPMK', 1);
    % ensure that the filename contains a full path specification,
    % otherwise the low-level function fails
    [p,n] = fileparts(filename);
    if isempty(p)
      filename = which(filename);
      [p,n] = fileparts(filename);
    end

    NEV = openNEV(filename,'noread','nosave');

    % searching for associated nsX file in same folder
    files=dir(strcat(fullfile(p,n),'.ns*'));
    if isempty(files)
      ft_error('no .ns* file associated to %s in %s',n,p);
    end

    %searching for nsX file with same sampling freq that NEV
    for i=1:numel(files)
      nsX_hdr = ft_read_header(fullfile(p,files(i).name),'chantype',chantype);
      if nsX_hdr.Fs == NEV.MetaTags.SampleRes
        hdr = nsX_hdr;
        break
      end
    end

    if isempty(hdr)
      ft_error('no .ns* file with same sampling frequency as %s (%i)',n,NEV.MetaTags.SampleRes);
    end

  case 'blackrock_nsx'
    ft_hastoolbox('NPMK', 1);
    % ensure that the filename contains a full path specification,
    % otherwise the low-level function fails
    p = fileparts(filename);
    if isempty(p)
      filename = which(filename);
    end

    orig = openNSx(filename, 'noread');
    channelstype=regexp({orig.ElectrodesInfo.Label},'[A-Za-z]+','match','once');
    if isfield(orig.ElectrodesInfo, 'ElectrodeID') % has been renamed into ChannelID
      for i=1:numel(orig.ElectrodesInfo)
        orig.ElectrodesInfo(i).ChannelID = orig.ElectrodesInfo(i).ElectrodeID;
      end
    end
    chaninfo=table({orig.ElectrodesInfo.ChannelID}',...
      transpose(deblank({orig.ElectrodesInfo.Label})),[channelstype]',...
      {orig.ElectrodesInfo.ConnectorBank}',{orig.ElectrodesInfo.ConnectorPin}',...
      transpose(deblank({orig.ElectrodesInfo.AnalogUnits})),...
      'VariableNames',{'id' 'label' 'chantype' 'bank' 'pin' 'unit'});

    if isempty(chantype)
      chantype = unique(channelstype,'stable');
    end

    % selecting channel according to chantype
    orig_label=deblank({orig.ElectrodesInfo.Label});
    orig_unit=deblank({orig.ElectrodesInfo.AnalogUnits});
    channels={}; channelstype={}; channelsunit={}; skipfactor=[];
    for c=1:length(chantype)
      chantype_split=strsplit(chantype{c},':');
      if numel(chantype_split) == 2
        chantype{c}=chantype_split{1};
        skipfactor=[skipfactor,str2double(chantype_split{2})];
      elseif numel(chantype_split) > 2
        ft_error('Use : to specify skipfactor, e.g. analog:10')
      end
      chan_sel=~cellfun(@isempty,regexp(orig_label,chantype{c}));
      if sum(chan_sel)==0
        if ~strcmp(chantype{c},'chaninfo')
          ft_error('unknown chantype %s, available channels are %s',chantype{c},strjoin(orig_label));
        end
      else
        channels=[channels, orig_label(chan_sel)];
        channelsunit=[channelsunit, orig_unit(chan_sel)];
        channelstype=[channelstype, repmat(chantype(c), 1, sum(chan_sel))];
      end
    end

    skipfactor=unique(skipfactor);
    if isempty(skipfactor)
      skipfactor=1;
    elseif length(skipfactor)>1
      ft_error('inconsistent skip factors across channels');
    end

    % If no channel selected issue error specifying available chantypes
    if isempty(channels)
      ft_error('No channel selected. Availabe chantypes are: %s', strjoin(unique(chaninfo.chantype)));
    end

    hdr.Fs          = orig.MetaTags.SamplingFreq/skipfactor;
    hdr.nChans      = length(channels);
    hdr.nSamples    = floor(orig.MetaTags.DataPoints/skipfactor);
    hdr.nSamplesPre = 0;
    hdr.nTrials     = 1; %?
    hdr.label       = deblank(channels)';
    hdr.chantype    = channelstype;
    hdr.chanunit    = channelsunit;
    hdr.orig        = orig;
    hdr.orig.chaninfo = chaninfo;
    hdr.orig.skipfactor = skipfactor;

  case {'brainvision_vhdr', 'brainvision_seg', 'brainvision_eeg', 'brainvision_dat'}
    orig = read_brainvision_vhdr(filename);
    hdr.Fs          = orig.Fs;
    hdr.nChans      = orig.NumberOfChannels;
    hdr.label       = orig.label;
    hdr.nSamples    = orig.nSamples;
    hdr.nSamplesPre = orig.nSamplesPre;
    hdr.nTrials     = orig.nTrials;
    hdr.orig        = orig;
    % assign the channel type and units for the known channels
    hdr.chanunit = orig.unit;
    hdr.chantype = repmat({'unknown'}, size(hdr.label));
    hdr.chantype(strcmp(hdr.chanunit, 'uV')) = {'eeg'}; % assume these to be EEG

  case 'brainvision_bvrh'
    % NOTE: this is based on two example test datasets, not sure how
    % well it generalizes

    [p, f, e] = fileparts(filename);
    [h, orig] = eeg_loadbvrf(p, [f, e]);
    orig      = orig{1}; % FIXME don't know yet what happens with more than one element in the array
    
    hdr.Fs     = orig.srate;
    hdr.nChans = orig.nbchan;
    hdr.label  = {orig.chanlocs.labels}';
    hdr.nSamples = orig.pnts;
    hdr.nSamplesPre = 0;
    hdr.nTrials     = orig.trials;
    hdr.orig        = removefields(orig, {'data' 'event'});

    % anecdotally, the two example files based on which this code was
    % written, each behave differently w.r.t. the contents of h.EEGModality
    if isstruct(h.EEGModality.Channels)
      hdr.chanunit    = {h.EEGModality.Channels.Unit}';
      hdr.chantype    = lower({h.EEGModality.Channels.Type}');
    elseif iscell(h.EEGModality.Channels)
      for i = 1:numel(h.EEGModality.Channels)
        hdr.chanunit{i,1} = h.EEGModality.Channels{i}.Unit;
        hdr.chantype{i,1} = lower(h.EEGModality.Channels{i}.Type);
      end
    end
  case 'bucn_nirs'
    orig = read_bucn_nirshdr(filename);
    hdr  = rmfield(orig, 'time');
    hdr.orig = orig;

  case 'ced_son'
    % check that the required low-level toolbox is available
    ft_hastoolbox('neuroshare', 1);
    % use the reading function supplied by Gijs van Elswijk
    orig = read_ced_son(filename,'readevents','no','readdata','no');
    orig = orig.header;
    % In Spike2, channels can have different sampling rates, units, length
    % etc. etc. Here, channels need to have to same properties.
    if length(unique([orig.samplerate]))>1
      ft_error('channels with different sampling rates are not supported');
    else
      hdr.Fs   = orig(1).samplerate;
    end
    hdr.nChans = length(orig);
    % nsamples of the channel with least samples
    hdr.nSamples    = min([orig.nsamples]);
    hdr.nSamplesPre = 0;
    % only continuous data supported
    if sum(strcmpi({orig.mode},'continuous')) < hdr.nChans
      ft_error('not all channels contain continuous data');
    else
      hdr.nTrials = 1;
    end
    hdr.label = {orig.label};

  case  'combined_ds'
    hdr = read_combined_ds(filename);

  case {'ctf_ds', 'ctf_meg4', 'ctf_res4'}
    % check the presence of the required low-level toolbox
    ft_hastoolbox('ctf', 1);

    % default for CTF is to remove the site-specific numbers from each channel name, e.g. 'MZC01-1706' becomes 'MZC01'
    splitlabel = ft_getopt(varargin, 'splitlabel', true);

    orig = readCTFds(filename);
    [p,f,e] = fileparts(filename);

    if cache
      % the code ends up here if cache==true AND the cached header was
      % still empty, i.e. it is reading the chunk for the first time.
      fid = fopen(fullfile(filename, sprintf('%s.res4', f)),'r','ieee-be');
      chunk = fread(fid, 'uint8=>uint8');
      fclose(fid);
      hdr.ctf_res4 = chunk;
    end
    
    if isempty(orig)
      % this is to deal with data from the 64 channel system and the error
      % readCTFds: .meg4 file header=MEG4CPT   Valid header options:  MEG41CP  MEG42CP
      ft_error('could not read CTF with this implementation, please try again with the ''ctf_old'' file format');
    end
    hdr.Fs           = orig.res4.sample_rate;
    hdr.nChans       = orig.res4.no_channels;
    hdr.nSamples     = orig.res4.no_samples;
    hdr.nSamplesPre  = orig.res4.preTrigPts;
    hdr.nTrials      = orig.res4.no_trials;
    hdr.label        = cellstr(orig.res4.chanNames);

    % read the balance coefficients, these are used to compute the synthetic gradients
    try
      coeftype = cellstr(char(orig.res4.scrr(:).coefType));
    catch
      coeftype = {};
    end
    try
      [alphaMEG,MEGlist,Refindex] = getCTFBalanceCoefs(orig,'NONE', 'T');
      orig.BalanceCoefs.none.alphaMEG  = alphaMEG;
      orig.BalanceCoefs.none.MEGlist   = MEGlist;
      orig.BalanceCoefs.none.Refindex  = Refindex;
    catch
      ft_warning('cannot read balancing coefficients for NONE');
    end
    if any(~cellfun(@isempty,strfind(coeftype, 'G1BR')))
      try
        [alphaMEG,MEGlist,Refindex] = getCTFBalanceCoefs(orig,'G1BR', 'T');
        orig.BalanceCoefs.G1BR.alphaMEG  = alphaMEG;
        orig.BalanceCoefs.G1BR.MEGlist   = MEGlist;
        orig.BalanceCoefs.G1BR.Refindex  = Refindex;
      catch
        ft_warning('cannot read balancing coefficients for G1BR');
      end
    end
    if any(~cellfun(@isempty,strfind(coeftype, 'G2BR')))
      try
        [alphaMEG,MEGlist,Refindex] = getCTFBalanceCoefs(orig, 'G2BR', 'T');
        orig.BalanceCoefs.G2BR.alphaMEG  = alphaMEG;
        orig.BalanceCoefs.G2BR.MEGlist   = MEGlist;
        orig.BalanceCoefs.G2BR.Refindex  = Refindex;
      catch
        ft_warning('cannot read balancing coefficients for G2BR');
      end
    end
    if any(~cellfun(@isempty,strfind(coeftype, 'G3BR')))
      try
        [alphaMEG,MEGlist,Refindex] = getCTFBalanceCoefs(orig, 'G3BR', 'T');
        orig.BalanceCoefs.G3BR.alphaMEG  = alphaMEG;
        orig.BalanceCoefs.G3BR.MEGlist   = MEGlist;
        orig.BalanceCoefs.G3BR.Refindex  = Refindex;
      catch
        ft_warning('cannot read balancing coefficients for G3BR');
      end
    end
    if any(~cellfun(@isempty,strfind(coeftype, 'G3AR')))
      try
        [alphaMEG,MEGlist,Refindex] = getCTFBalanceCoefs(orig, 'G3AR', 'T');
        orig.BalanceCoefs.G3AR.alphaMEG  = alphaMEG;
        orig.BalanceCoefs.G3AR.MEGlist   = MEGlist;
        orig.BalanceCoefs.G3AR.Refindex  = Refindex;
      catch
        % May not want a warning here if these are not commonly used.
        % Already get a (fprintf) warning from getCTFBalanceCoefs.m
        % ft_warning('cannot read balancing coefficients for G3AR');
      end
    end
    % add a gradiometer structure for forward and inverse modelling
    try
      [grad, elec] = ctf2grad(orig, strcmp(coordsys, 'dewar'), coilaccuracy, coildeffile);
      if ~isempty(grad)
        hdr.grad = grad;
      end
      if ~isempty(elec)
        hdr.elec = elec;
      end
    catch
      % this fails if the res4 file is not correctly closed, e.g. during realtime processing
      tmp = lasterror;
      disp(tmp.message);
      ft_warning('could not construct gradiometer definition from the header');
    end

    % for realtime analysis EOF chasing the res4 does not correctly
    % estimate the number of samples, so we compute it on the fly from the
    % meg4 file sizes.
    sz = 0;
    files = dir([filename '/*.*meg4']);
    for j=1:numel(files)
      sz = sz + files(j).bytes;
    end
    hdr.nTrials = floor((sz - 8) / (hdr.nChans*4) / hdr.nSamples);

    % add the original header details
    hdr.orig = orig;

    if cache
      hdr.details = dir(headerfile);
      cacheheader = hdr;
      hdr = rmfield(hdr, 'details');
    end

  case {'ctf_old', 'read_ctf_res4'}
    % read it using the open-source MATLAB code that originates from CTF and that was modified by the FCDC
    orig             = read_ctf_res4(headerfile);
    hdr.Fs           = orig.Fs;
    hdr.nChans       = orig.nChans;
    hdr.nSamples     = orig.nSamples;
    hdr.nSamplesPre  = orig.nSamplesPre;
    hdr.nTrials      = orig.nTrials;
    hdr.label        = orig.label;
    % add a gradiometer structure for forward and inverse modelling
    try
      hdr.grad = ctf2grad(orig, strcmp(coordsys, 'dewar'));
    catch
      % this fails if the res4 file is not correctly closed, e.g. during realtime processing
      tmp = lasterror;
      disp(tmp.message);
      ft_warning('could not construct gradiometer definition from the header');
    end
    % add the original header details
    hdr.orig = orig;

  case 'ctf_read_res4'
    % check that the required low-level toolbos ix available
    ft_hastoolbox('eegsf', 1);
    % read it using the CTF importer from the NIH and Daren Weber
    orig = ctf_read_res4(fileparts(headerfile), 0);
    % convert the header into a structure that FieldTrip understands
    hdr              = [];
    hdr.Fs           = orig.setup.sample_rate;
    hdr.nChans       = length(orig.sensor.info);
    hdr.nSamples     = orig.setup.number_samples;
    hdr.nSamplesPre  = orig.setup.pretrigger_samples;
    hdr.nTrials      = orig.setup.number_trials;
    for i=1:length(orig.sensor.info)
      hdr.label{i}   = orig.sensor.info(i).label;
    end
    hdr.label        = hdr.label(:);
    % add a gradiometer structure for forward and inverse modelling
    try
      hdr.grad = ctf2grad(orig);
    catch
      % this fails if the res4 file is not correctly closed, e.g. during realtime processing
      tmp = lasterror;
      disp(tmp.message);
      ft_warning('could not construct gradiometer definition from the header');
    end
    % add the original header details
    hdr.orig = orig;

  case 'ctf_shm'
    % read the header information from shared memory
    hdr = read_shm_header(filename);


  case {'curry_dat', 'curry_cdt'}
    orig            = load_curry_data_file(filename);
    hdr             = [];
    hdr.Fs          = orig.fFrequency;
    hdr.nChans      = orig.nChannels;
    hdr.nSamples    = orig.nSamples;
    hdr.nSamplesPre = sum(orig.time<0);
    hdr.nTrials     = orig.nTrials;
    hdr.label       = orig.labels(:);
    hdr.orig        = orig;

  case 'dataq_wdq'
    orig            = read_wdq_header(filename);
    hdr             = [];
    hdr.Fs          = orig.fsample;
    hdr.nChans      = orig.nchan;
    hdr.nSamples    = orig.nbytesdat/(2*hdr.nChans);
    hdr.nSamplesPre = 0;
    hdr.nTrials     = 1;
    for k = 1:hdr.nChans
      if isfield(orig.chanhdr(k), 'annot') && ~isempty(orig.chanhdr(k).annot)
        hdr.label{k,1} = orig.chanhdr(k).annot;
      else
        hdr.label{k,1} = orig.chanhdr(k).label;
      end
    end

    % add the original header details
    hdr.orig  = orig;

  case {'deymed_ini' 'deymed_dat'}
    % the header is stored in a *.ini file
    orig            = read_deymed_ini(headerfile);
    hdr             = [];
    hdr.Fs          = orig.Fs;
    hdr.nChans      = orig.nChans;
    hdr.nSamples    = orig.nSamples;
    hdr.nSamplesPre = 0;
    hdr.nTrials     = 1;
    hdr.label       = orig.label(:);
    hdr.orig        = orig; % remember the original details

  case 'dhn_med10'
    ft_hastoolbox('mayo_mef', 1); % make sure mayo_mef exists
    hdr = read_dhn_med10(filename, password);

  case 'edf'
    % this reader is largely similar to the bdf reader
    if isempty(chanindx)
      hdr = read_edf(filename);
    else
      % this will keep the non-selected channels hidden from the user
      hdr = read_edf(filename, [], chanindx);
    end

  case 'eep_avr'
    % check that the required low-level toolbox is available
    ft_hastoolbox('eeprobe', 1);
    % read the whole average and keep only header info (it is a bit silly, but the easiest to do here)
    hdr = read_eep_avr(filename);
    hdr.Fs          = hdr.rate;
    hdr.nChans      = size(hdr.data,1);
    hdr.nSamples    = size(hdr.data,2);
    hdr.nSamplesPre = hdr.xmin*hdr.rate/1000;
    hdr.nTrials     = 1;        % it can always be interpreted as continuous data
    % remove the data and variance if present
    hdr = removefields(hdr, {'data', 'variance'});

  case 'eep_cnt'
    % check that the required low-level toolbox is available
    ft_hastoolbox('eeprobe', 1);
    % read the first sample from the continuous data, this will also return the header
    orig = read_eep_cnt(filename, 1, 1);
    hdr.Fs          = orig.rate;
    hdr.nSamples    = orig.nsample;
    hdr.nSamplesPre = 0;
    hdr.label       = orig.label;
    hdr.nChans      = orig.nchan;
    hdr.nTrials     = 1;        % it can always be interpreted as continuous data
    hdr.orig        = orig;     % remember the original details


  case 'eeglab_set'
    hdr = read_eeglabheader(filename);

  case 'eeglab_erp'
    hdr = read_erplabheader(filename);

  case 'emotiv_mat'
    % This is a MATLAB *.mat file that is created using the Emotiv MATLAB
    % example code. It contains a 25xNsamples matrix and some other stuff.
    orig = load(filename);
    hdr.Fs          = 128;
    hdr.nChans      = 25;
    hdr.nSamples    = size(orig.data_eeg,1);
    hdr.nSamplesPre = 0;
    hdr.nTrials     = 1;
    hdr.label       = {'ED_COUNTER','ED_INTERPOLATED','ED_RAW_CQ','ED_AF3','ED_F7','ED_F3','ED_FC5','ED_T7','ED_P7','ED_O1','ED_O2','ED_P8','ED_T8','ED_FC6','ED_F4','ED_F8','ED_AF4','ED_GYROX','ED_GYROY','ED_TIMESTAMP','ED_ES_TIMESTAMP','ED_FUNC_ID','ED_FUNC_VALUE','ED_MARKER','ED_SYNC_SIGNAL'};
    % store the complete information in hdr.orig
    % ft_read_data and ft_read_event will get it from there
    hdr.orig        = orig;

  case 'eyelink_asc'
    asc = read_eyelink_asc(filename);
    hdr.nChans              = size(asc.dat,1);
    hdr.nSamples            = size(asc.dat,2);
    hdr.nSamplesPre         = 0;
    hdr.nTrials             = 1;
    hdr.FirstTimeStamp      = asc.dat(1,1);
    hdr.TimeStampPerSample  = median(diff(asc.dat(1,:)));
    hdr.Fs                  = 1000/hdr.TimeStampPerSample;  % these timestamps are in miliseconds
    % give this warning only once
    ft_warning('creating fake channel names');
    for i=1:hdr.nChans
      hdr.label{i} = sprintf('%d', i);
    end

    % remember all header and data details upon request
    if cache
      hdr.orig = asc;
    else
      % remember the original header details
      hdr.orig = removefields(asc, 'dat');
    end

  case  'spmeeg_mat'
    hdr = read_spmeeg_header(filename);

  case  'ced_spike6mat'
    hdr = read_spike6mat_header(filename);

  case 'egi_egia'
    [fhdr,chdr,ename,cnames,fcom,ftext] = read_egis_header(filename);
    [p, f, x]       = fileparts(filename);

    if any(chdr(:,4)-chdr(1,4))
      ft_error('Sample rate not the same for all cells.');
    end

    hdr.Fs          = chdr(1,4); %making assumption that sample rate is same for all cells
    hdr.nChans      = fhdr(19);
    for i = 1:hdr.nChans
      % this should be consistent with ft_senslabel
      hdr.label{i,1}  = ['E' num2str(i)];
    end
    %since NetStation does not properly set the fhdr(11) field, use the number of subjects from the chdr instead
    hdr.nTrials     = chdr(1,2)*fhdr(18); %number of trials is numSubjects * numCells
    hdr.nSamplesPre = ceil(fhdr(14)/(1000/hdr.Fs));

    if any(chdr(:,3)-chdr(1,3))
      ft_error('Number of samples not the same for all cells.');
    end

    hdr.nSamples    = chdr(1,3); %making assumption that number of samples is same for all cells

    % remember the original header details
    hdr.orig.fhdr   = fhdr;
    hdr.orig.chdr   = chdr;
    hdr.orig.ename  = ename;
    hdr.orig.cnames = cnames;
    hdr.orig.fcom   = fcom;
    hdr.orig.ftext  = ftext;

  case 'egi_egis'
    [fhdr,chdr,ename,cnames,fcom,ftext] = read_egis_header(filename);
    [p, f, x]       = fileparts(filename);

    if any(chdr(:,4)-chdr(1,4))
      ft_error('Sample rate not the same for all cells.');
    end

    hdr.Fs          = chdr(1,4); %making assumption that sample rate is same for all cells
    hdr.nChans      = fhdr(19);
    for i = 1:hdr.nChans
      % this should be consistent with ft_senslabel
      hdr.label{i,1}  = ['E' num2str(i)];
    end
    hdr.nTrials     = sum(chdr(:,2));
    hdr.nSamplesPre = ceil(fhdr(14)/(1000/hdr.Fs));
    % assuming that a utility was used to insert the correct baseline
    % duration into the header since it is normally absent. This slot is
    % actually allocated to the age of the subject, although NetStation
    % does not use it when generating an EGIS session file.

    if any(chdr(:,3)-chdr(1,3))
      ft_error('Number of samples not the same for all cells.');
    end

    hdr.nSamples    = chdr(1,3); %making assumption that number of samples is same for all cells

    % remember the original header details
    hdr.orig.fhdr   = fhdr;
    hdr.orig.chdr   = chdr;
    hdr.orig.ename  = ename;
    hdr.orig.cnames = cnames;
    hdr.orig.fcom   = fcom;
    hdr.orig.ftext  = ftext;

  case 'egi_sbin'
    [header_array, CateNames, CatLengths, preBaseline] = read_sbin_header(filename);
    [p, f, x]       = fileparts(filename);

    hdr.Fs          = header_array(9);
    hdr.nChans      = header_array(10);
    for i = 1:hdr.nChans
      % this should be consistent with ft_senslabel
      hdr.label{i,1}  = ['E' num2str(i)];
    end
    hdr.nTrials     = header_array(15);
    hdr.nSamplesPre = preBaseline;

    hdr.nSamples    = header_array(16); % making assumption that number of samples is same for all cells

    % remember the original header details
    hdr.orig.header_array   = header_array;
    hdr.orig.CateNames   = CateNames;
    hdr.orig.CatLengths  = CatLengths;

  case 'egi_mff_v1'
    % The following represents the code that was written by Ingrid, Robert
    % and Giovanni to get started with the EGI mff dataset format. It might
    % not support all details of the file formats.
    %
    % An alternative implementation has been provided by EGI, this is
    % released as fieldtrip/external/egi_mff and referred further down in
    % this function as 'egi_mff_v2'.
    %
    % An more recent implementation has been provided by EGI and Arno Delorme, this
    % is released as https://github.com/arnodelorme/mffmatlabio and referred further
    % down in this function as 'egi_mff_v3'.

    if ~usejava('jvm')
      ft_error('the xml2struct requires MATLAB to be running with the Java virtual machine (JVM)');
      % an alternative implementation which does not require the JVM but runs much slower is
      % available from http://www.mathworks.com/matlabcentral/fileexchange/6268-xml4mat-v2-0
    end

    % get header info from .bin files
    binfiles = dir(fullfile(filename, 'signal*.bin'));
    if isempty(binfiles)
      ft_error('could not find any signal.bin in mff directory')
    end

    orig = [];
    for iSig = 1:length(binfiles)
      signalname = binfiles(iSig).name;
      fullsignalname = fullfile(filename, signalname);
      orig.signal(iSig).blockhdr = read_mff_bin(fullsignalname);
    end

    % get hdr info from xml files
    ws = ft_warning('off', 'MATLAB:REGEXP:deprecated'); % due to some small code xml2struct
    xmlfiles = dir( fullfile(filename, '*.xml'));
    disp('reading xml files to obtain header info...')
    for i = 1:numel(xmlfiles)
      if strcmpi(xmlfiles(i).name(1:2), '._') % Mac sometimes creates this useless files, don't use them
      elseif strcmpi(xmlfiles(i).name(1:6), 'Events') % don't read in events here, can take a lot of time, and we can do that in ft_read_event
      else
        fieldname     = xmlfiles(i).name(1:end-4);
        filename_xml  = fullfile(filename, xmlfiles(i).name);
        orig.xml.(fieldname) = xml2struct(filename_xml);
      end
    end
    ft_warning(ws); % revert the warning state

    % epochs.xml seems the most common version, but epoch.xml might also
    % occur, so use only one name
    if isfield(orig.xml, 'epoch')
      orig.xml.epochs = orig.xml.epoch;
      orig.xml = rmfield(orig.xml, 'epoch');
    end

    % make hdr according to FieldTrip rules
    hdr = [];
    Fs = zeros(length(orig.signal),1);
    nChans = zeros(length(orig.signal),1);
    nSamples = zeros(length(orig.signal),1);

    for iSig = 1:length(orig.signal)
      Fs(iSig)      = orig.signal(iSig).blockhdr(1).fsample(1);
      nChans(iSig)  = orig.signal(iSig).blockhdr(1).nsignals;
      % the number of samples per block can be different
      nSamples_Block = zeros(length(orig.signal(iSig).blockhdr),1);
      for iBlock  = 1:length(orig.signal(iSig).blockhdr)
        nSamples_Block(iBlock) = orig.signal(iSig).blockhdr(iBlock).nsamples(1);
      end
      nSamples(iSig) = sum(nSamples_Block);
    end

    if length(unique(Fs)) > 1 || length(unique(nSamples)) > 1
      ft_error('Fs and nSamples should be the same in all signals')
    end

    hdr.Fs          = Fs(1);
    hdr.nChans      = sum(nChans);
    hdr.nSamplesPre = 0;
    hdr.nSamples    = nSamples(1);
    hdr.nTrials     = 1;

    % get channel labels for signal 1 (main net), otherwise create them
    if isfield(orig.xml, 'sensorLayout') % assuming that signal1 is hdEEG sensornet, and channels are in xml file sensorLayout
      for iSens = 1:numel(orig.xml.sensorLayout.sensors)
        if ~isempty(orig.xml.sensorLayout.sensors(iSens).sensor.name) && ~(isstruct(orig.xml.sensorLayout.sensors(iSens).sensor.name) && numel(fieldnames(orig.xml.sensorLayout.sensors(iSens).sensor.name))==0)
          %only get name when channel is EEG (type 0), or REF (type 1),
          %rest are non interesting channels like place holders and COM and should not be added.
          if strcmp(orig.xml.sensorLayout.sensors(iSens).sensor.type, '0') || strcmp(orig.xml.sensorLayout.sensors(iSens).sensor.type, '1')
            % get the sensor name from the datafile
            hdr.label{iSens} = orig.xml.sensorLayout.sensors(iSens).sensor.name;
          end
        elseif strcmp(orig.xml.sensorLayout.sensors(iSens).sensor.type, '0') % EEG chan
          % this should be consistent with ft_senslabel
          hdr.label{iSens} = ['E' num2str(orig.xml.sensorLayout.sensors(iSens).sensor.number)];
        elseif strcmp(orig.xml.sensorLayout.sensors(iSens).sensor.type, '1') % REF chan
          % ingnie: I now choose REF as name for REF channel since our discussion see bug 1407. Arbitrary choice...
          hdr.label{iSens} = ['REF' num2str(iSens)];
        else
          % non interesting channels like place holders and COM
        end
      end
      % check if the amount of lables corresponds with nChannels in signal 1
      if length(hdr.label) == nChans(1)
        % good
      elseif length(hdr.label) > orig.signal(1).blockhdr(1).nsignals
        ft_warning('found more lables in xml.sensorLayout than channels in signal 1, thus can not use info in sensorLayout, creating labels on the fly')
        for iSens = 1:orig.signal(1).blockhdr(1).nsignals
          % this should be consistent with ft_senslabel
          hdr.label{iSens} = ['E' num2str(iSens)];
        end
      else
        ft_warning('found less lables in xml.sensorLayout than channels in signal 1, thus can not use info in sensorLayout, creating labels on the fly')
        for iSens = 1:orig.signal(1).blockhdr(1).nsignals
          % this should be consistent with ft_senslabel
          hdr.label{iSens} = ['E' num2str(iSens)];
        end
      end
      % get lables for other signals
      if length(orig.signal) == 2
        if isfield(orig.xml, 'pnsSet') % signal2 is PIB box, and lables are in xml file pnsSet
          nbEEGchan = length(hdr.label);
          for iSens = 1:numel(orig.xml.pnsSet.sensors)
            hdr.label{nbEEGchan+iSens} = num2str(orig.xml.pnsSet.sensors(iSens).sensor.name);
          end
          if length(hdr.label) == orig.signal(1).blockhdr(1).nsignals + orig.signal(2).blockhdr(1).nsignals
            % good
          elseif length(hdr.label) < orig.signal(1).blockhdr(1).nsignals + orig.signal(2).blockhdr(1).nsignals
            ft_warning('found less lables in xml.pnsSet than channels in signal 2, labeling with s2_unknownN instead')
            for iSens = length(hdr.label)+1 : orig.signal(1).blockhdr(1).nsignals + orig.signal(2).blockhdr(1).nsignals
              hdr.label{iSens} = ['s2_unknown', num2str(iSens)];
            end
          else
            ft_warning('found more lables in xml.pnsSet than channels in signal 2, thus can not use info in pnsSet, and labeling with s2_eN instead')
            for iSens = orig.signal(1).blockhdr(1).nsignals+1 : orig.signal(1).blockhdr(1).nsignals + orig.signal(2).blockhdr(1).nsignals
              hdr.label{iSens} = ['s2_E', num2str(iSens)];
            end
          end
        else % signal2 is not PIBbox
          ft_warning('creating channel labels for signal 2 on the fly')
          for iSens = 1:orig.signal(2).blockhdr(1).nsignals
            hdr.label{end+1} = ['s2_E', num2str(iSens)];
          end
        end
      elseif length(orig.signal) > 2
        % loop over signals and label channels accordingly
        ft_warning('creating channel labels for signal 2 to signal N on the fly')
        for iSig = 2:length(orig.signal)
          for iSens = 1:orig.signal(iSig).blockhdr(1).nsignals
            if iSig == 1 && iSens == 1
              hdr.label{1} = ['s',num2str(iSig),'_E', num2str(iSens)];
            else
              hdr.label{end+1} = ['s',num2str(iSig),'_E', num2str(iSens)];
            end
          end
        end
      end
    else % no xml.sensorLayout present
      ft_warning('no sensorLayout found in xml files, creating channel labels on the fly')
      for iSig = 1:length(orig.signal)
        for iSens = 1:orig.signal(iSig).blockhdr(1).nsignals
          if iSig == 1 && iSens == 1
            hdr.label{1} = ['s',num2str(iSig),'_E', num2str(iSens)];
          else
            hdr.label{end+1} = ['s',num2str(iSig),'_E', num2str(iSens)];
          end
        end
      end
    end

    % check if multiple epochs are present
    if isfield(orig.xml,'epochs')
      % add info to header about which sample correspond to which epochs, becasue this is quite hard for user to get...
      epochdef = zeros(length(orig.xml.epochs),3);
      for iEpoch = 1:length(orig.xml.epochs)
        if iEpoch == 1
          epochdef(iEpoch,1) = round(str2double(orig.xml.epochs(iEpoch).epoch.beginTime)./(1000000./hdr.Fs))+1;
          epochdef(iEpoch,2) = round(str2double(orig.xml.epochs(iEpoch).epoch.endTime  )./(1000000./hdr.Fs));
          epochdef(iEpoch,3) = round(str2double(orig.xml.epochs(iEpoch).epoch.beginTime)./(1000000./hdr.Fs)); % offset corresponds to timing
        else
          NbSampEpoch = round(str2double(orig.xml.epochs(iEpoch).epoch.endTime)./(1000000./hdr.Fs) - str2double(orig.xml.epochs(iEpoch).epoch.beginTime)./(1000000./hdr.Fs));
          epochdef(iEpoch,1) = epochdef(iEpoch-1,2) + 1;
          epochdef(iEpoch,2) = epochdef(iEpoch-1,2) + NbSampEpoch;
          epochdef(iEpoch,3) = round(str2double(orig.xml.epochs(iEpoch).epoch.beginTime)./(1000000./hdr.Fs)); % offset corresponds to timing
        end
      end

      if epochdef(end,2) ~= hdr.nSamples
        % check for NS 4.5.4 picosecond timing
        if (epochdef(end,2)/1000) == hdr.nSamples
          for iEpoch=1:size(epochdef,1)
            epochdef(iEpoch,1) = ((epochdef(iEpoch,1)-1)/1000)+1;
            epochdef(iEpoch,2) = epochdef(iEpoch,2)/1000;
            epochdef(iEpoch,3) = epochdef(iEpoch,3)/1000;
          end
          ft_warning('mff apparently generated by NetStation 4.5.4.  Adjusting time scale to microseconds from nanoseconds.');
        else
          ft_error('number of samples in all epochs do not add up to total number of samples')
        end
      end

      epochLengths = epochdef(:,2)-epochdef(:,1)+1;
      if ~any(diff(epochLengths))
        hdr.nSamples = epochLengths(1);
        hdr.nTrials  = length(epochLengths);

      else
        ft_warning('the data contains multiple epochs with variable length, possibly causing discontinuities in the data')
        % sanity check
        if epochdef(end,2) ~= hdr.nSamples
          % check for NS 4.5.4 picosecond timing
          if (epochdef(end,2)/1000) == hdr.nSamples
            for iEpoch=1:size(epochdef,1)
              epochdef(iEpoch,1)=((epochdef(iEpoch,1)-1)/1000)+1;
              epochdef(iEpoch,2)=epochdef(iEpoch,2)/1000;
              epochdef(iEpoch,3)=epochdef(iEpoch,3)/1000;
            end
            disp('mff apparently generated by NetStation 4.5.4.  Adjusting time scale to microseconds from nanoseconds.');
          else
            ft_error('number of samples in all epochs do not add up to total number of samples')
          end
        end
      end
      orig.epochdef = epochdef;
    end
    hdr.orig = orig;

  case 'egi_mff_v2'
    % ensure that the EGI_MFF_V2 toolbox is on the path
    ft_hastoolbox('egi_mff_v2', 1);

    %%%%%%%%%%%%%%%%%%%%%%
    %workaround for MATLAB bug resulting in global variables being cleared
    globalTemp=cell(0);
    globalList=whos('global');
    varList=whos;
    for i=1:length(globalList)
      eval(['global ' globalList(i).name ';']);
      eval(['globalTemp{end+1}=' globalList(i).name ';']);
    end
    %%%%%%%%%%%%%%%%%%%%%%

    % ensure that the JVM is running and the jar file is on the path
    mff_setup;

    %%%%%%%%%%%%%%%%%%%%%%
    %workaround for MATLAB bug resulting in global variables being cleared
    varNames={varList.name};
    for i=1:length(globalList)
      eval(['global ' globalList(i).name ';']);
      eval([globalList(i).name '=globalTemp{i};']);
      if ~any(strcmp(globalList(i).name,varNames)) %was global variable originally out of scope?
        eval(['clear ' globalList(i).name ';']); %clears link to global variable without affecting it
      end
    end
    clear globalTemp globalList varNames varList;
    %%%%%%%%%%%%%%%%%%%%%%

    if isunix && filename(1)~=filesep
      % add the full path to the dataset directory
      filename = fullfile(pwd, filename);
    elseif ispc && ~any(strcmp(filename(2),{':','\'}))
      % add the full path, including drive letter or slashes as needed.
      filename = fullfile(pwd, filename);
    end

    hdr = read_mff_header(filename);

  case {'egi_mff_v3' 'egi_mff'} % this is the default
    ft_hastoolbox('mffmatlabio', 1);
    hdr = mff_fileio_read_header(filename);

  case 'fcdc_buffer'
    % read from a networked buffer for realtime analysis
    [host, port] = filetype_check_uri(filename);

    if retry
      orig = [];
      while isempty(orig)
        try
          % try reading the header, catch the error and retry
          orig = buffer('get_hdr', [], host, port);
        catch
          ft_warning('could not read header from %s, retrying in 1 second', filename);
          pause(1);
        end
      end % while
    else
      % try reading the header only once, give error if it fails
      orig = buffer('get_hdr', [], host, port);
    end % if retry

    % construct the standard header elements
    hdr.Fs          = orig.fsample;
    hdr.nChans      = orig.nchans;
    hdr.nSamples    = orig.nsamples;
    hdr.nSamplesPre = 0;  % since continuous
    hdr.nTrials     = 1;  % since continuous
    hdr.orig        = []; % this will contain the chunks (if present)

    % add the contents of attached NEUROMAG_HEADER chunk after decoding to MATLAB structure
    if isfield(orig, 'neuromag_header')
      if isempty(cachechunk)
        % this only needs to be decoded once
        cachechunk = decode_fif(orig);
      end

      % convert to FieldTrip format header
      hdr.label       = cachechunk.ch_names(:);
      hdr.nChans      = cachechunk.nchan;
      hdr.Fs          = cachechunk.sfreq;

      % add a gradiometer structure for forward and inverse modelling
      try
        [grad, elec] = mne2grad(cachechunk, true, coilaccuracy, coildeffile); % the coordsys is 'dewar'
        if ~isempty(grad)
          hdr.grad = grad;
        end
        if ~isempty(elec)
          hdr.elec = elec;
        end
      catch
        disp(lasterr);
      end

      % store the original details
      hdr.orig = cachechunk;
    end

    % add the contents of attached CTF_RES4 chunk after decoding to MATLAB structure
    if isfield(orig, 'ctf_res4')
      if isempty(cachechunk)
        % this only needs to be decoded once
        cachechunk = decode_res4(orig.ctf_res4);
      end
      % copy the gradiometer details
      hdr.grad = cachechunk.grad;
      hdr.orig = cachechunk.orig;
      if isfield(orig, 'channel_names')
        % get the same selection of channels from the two chunks
        [selbuf, selres4] = match_str(orig.channel_names, cachechunk.label);
        if length(selres4)<length(orig.channel_names)
          ft_error('the res4 chunk did not contain all channels')
        end
        % copy some of the channel details
        hdr.label     = cachechunk.label(selres4);
        hdr.chantype  = cachechunk.chantype(selres4);
        hdr.chanunit  = cachechunk.chanunit(selres4);
        % add the channel names chunk as well
        hdr.orig.channel_names = orig.channel_names;
      end
      % add the raw chunk as well
      hdr.orig.ctf_res4 = orig.ctf_res4;
    end

    % add the contents of attached NIFTI_1 chunk after decoding to MATLAB structure
    if isfield(orig, 'nifti_1')
      hdr.nifti_1 = decode_nifti1(orig.nifti_1);
      % add the raw chunk as well
      hdr.orig.nifti_1 = orig.nifti_1;
    end

    % add the contents of attached SiemensAP chunk after decoding to MATLAB structure
    if isfield(orig, 'siemensap') && exist('sap2matlab')==3 % only run this if MEX file is present
      hdr.siemensap = sap2matlab(orig.siemensap);
      % add the raw chunk as well
      hdr.orig.siemensap = orig.siemensap;
    end

    if ~isfield(hdr, 'label')
      % prevent overwriting the labels that we might have gotten from a RES4 chunk
      if isfield(orig, 'channel_names')
        hdr.label = orig.channel_names;
      else
        hdr.label = cell(hdr.nChans,1);
        if hdr.nChans < 2000 % don't do this for fMRI etc.
          ft_warning('creating fake channel names');        % give this warning only once
          for i=1:hdr.nChans
            hdr.label{i} = sprintf('%d', i);
          end
        else
          ft_warning('skipping fake channel names');        % give this warning only once
          checkUniqueLabels = false;
        end
      end
    end

    if ~isfield(hdr, 'chantype')
      % prevent overwriting the chantypes that we might have gotten from a RES4 chunk
      hdr.chantype = cell(hdr.nChans,1);
      if hdr.nChans < 2000 % don't do this for fMRI etc.
        hdr.chantype = repmat({'unknown'}, 1, hdr.nChans);
      end
    end

    if ~isfield(hdr, 'chanunit')
      % prevent overwriting the chanunits that we might have gotten from a RES4 chunk
      hdr.chanunit = cell(hdr.nChans,1);
      if hdr.nChans < 2000 % don't do this for fMRI etc.
        hdr.chanunit = repmat({'unknown'}, 1, hdr.nChans);
      end
    end

    hdr.orig.bufsize = orig.bufsize;


  case 'fcdc_buffer_offline'
    [hdr, nameFlag] = read_buffer_offline_header(headerfile);
    switch nameFlag
      case 0
        % no labels generated (fMRI etc)
        checkUniqueLabels = false; % no need to check these
      case 1
        % has generated fake channels
        % give this warning only once
        ft_warning('creating fake channel names');
        checkUniqueLabels = false; % no need to check these
      case 2
        % got labels from chunk, check those
        checkUniqueLabels = true;
    end

  case 'fcdc_matbin'
    % this is multiplexed data in a *.bin file, accompanied by a MATLAB file containing the header
    load(headerfile, 'hdr');

  case 'fcdc_mysql'
    % check that the required low-level toolbox is available
    ft_hastoolbox('mysql', 1);
    % read from a MySQL server listening somewhere else on the network
    db_open(filename);
    if db_blob
      hdr = db_select_blob('fieldtrip.header', 'msg', 1);
    else
      hdr = db_select('fieldtrip.header', {'nChans', 'nSamples', 'nSamplesPre', 'Fs', 'label'}, 1);
      hdr.label = mxDeserialize(hdr.label);
    end

  case 'gtec_hdf5'
    % check that the required low-level toolbox is available
    ft_hastoolbox('gtec', 1);
    % there is only a precompiled *.p reader that reads the whole file at once
    orig = ghdf5read(filename);
    for i=1:numel(orig.RawData.AcquisitionTaskDescription.ChannelProperties.ChannelProperties)
      lab = orig.RawData.AcquisitionTaskDescription.ChannelProperties.ChannelProperties(i).ChannelName;
      typ = orig.RawData.AcquisitionTaskDescription.ChannelProperties.ChannelProperties(1).ChannelType;
      if isnumeric(lab)
        hdr.label{i} = num2str(lab);
      else
        hdr.label{i} = lab;
      end
      if ischar(typ)
        hdr.chantype{i} = lower(typ);
      else
        hdr.chantype{i} = 'unknown';
      end
    end
    hdr.Fs          = orig.RawData.AcquisitionTaskDescription.SamplingFrequency;
    hdr.nChans      = size(orig.RawData.Samples, 1);
    hdr.nSamples    = size(orig.RawData.Samples, 2);
    hdr.nSamplesPre = 0;
    hdr.nTrials     = 1; % assume continuous data, not epoched
    assert(orig.RawData.AcquisitionTaskDescription.NumberOfAcquiredChannels==hdr.nChans, 'inconsistent number of channels');
    % store the data and event information when requested (default)
    if cache
      hdr.orig = orig;
    end

  case 'gtec_mat'
    % this is a simple MATLAB format, it contains a log and a names variable
    tmp = load(headerfile);
    log   = tmp.log;
    names = tmp.names;

    hdr.label = cellstr(names);
    hdr.nChans = size(log,1);
    hdr.nSamples = size(log,2);
    hdr.nSamplesPre = 0;
    hdr.nTrials = 1; % assume continuous data, not epoched

    % compute the sampling frequency from the time channel
    sel = strcmp(hdr.label, 'Time');
    time = log(sel,:);

    hdr.Fs = 1./(time(2)-time(1));

    % also remember the complete data upon request
    if cache
      hdr.orig.log = log;
      hdr.orig.names = names;
    end

  case 'gdf'
    % this requires the biosig toolbox
    ft_hastoolbox('BIOSIG', 1);
    % In the case that the gdf files are written by one of the FieldTrip
    % realtime applications, such as biosig2ft, the gdf recording can be
    % split over multiple 1GB files. The sequence of files is then
    %   filename.gdf   <- this is the one that should be specified as the filename/dataset
    %   filename_1.gdf
    %   filename_2.gdf
    %   ...

    [p, f, x] = fileparts(filename);
    if exist(sprintf('%s_%d%s', fullfile(p, f), 1, x), 'file')
      % there are multiple files, count the number of additional files (excluding the first one)
      count = 0;
      while exist(sprintf('%s_%d%s', fullfile(p, f), count+1, x), 'file')
        count = count+1;
      end
      hdr = read_biosig_header(filename);
      for i=1:count
        hdr(i+1) = read_biosig_header(sprintf('%s_%d%s', fullfile(p, f), i, x));
        % do some sanity checks
        if hdr(i+1).nChans~=hdr(1).nChans
          ft_error('multiple GDF files detected that should be appended, but the channel count is inconsistent');
        elseif hdr(i+1).Fs~=hdr(1).Fs
          ft_error('multiple GDF files detected that should be appended, but the sampling frequency is inconsistent');
        elseif ~isequal(hdr(i+1).label, hdr(1).label)
          ft_error('multiple GDF files detected that should be appended, but the channel names are inconsistent');
        end
      end % for count
      % combine all headers into one
      combinedhdr             = [];
      combinedhdr.Fs          = hdr(1).Fs;
      combinedhdr.nChans      = hdr(1).nChans;
      combinedhdr.nSamples    = sum([hdr.nSamples].*[hdr.nTrials]);
      combinedhdr.nSamplesPre = 0;
      combinedhdr.nTrials     = 1;
      combinedhdr.label       = hdr(1).label;
      combinedhdr.orig        = hdr; % include all individual file details
      hdr = combinedhdr;

    else
      % there is only a single file
      hdr = read_biosig_header(filename);
      % the GDF format is always continuous
      hdr.nSamples = hdr.nSamples * hdr.nTrials;
      hdr.nTrials = 1;
      hdr.nSamplesPre = 0;
    end % if single or multiple gdf files

  case {'homer_nirs'}
    % Homer files are MATLAB files in disguise
    % see https://www.nitrc.org/plugins/mwiki/index.php/homer2:Homer_Input_Files#NIRS_data_file_format
    nirs = load(filename, '-mat');
    % convert it to a raw data structure according to FT_DATATYPE_RAW
    data = homer2fieldtrip(nirs);
    % get the header information as structure
    hdr = ft_fetch_header(data);

  case {'itab_raw' 'itab_mhd'}
    % read the full header information frtom the binary header structure
    header_info = read_itab_mhd(headerfile);

    % these are the channels that are visible to FieldTrip
    chansel = 1:header_info.nchan;

    % convert the header information into a FieldTrip compatible format
    hdr.nChans      = length(chansel);
    hdr.label       = {header_info.ch(chansel).label};
    hdr.label       = hdr.label(:);  % should be column vector
    hdr.Fs          = header_info.smpfq;
    % it will always be continuous data
    hdr.nSamples    = header_info.ntpdata;
    hdr.nSamplesPre = 0; % it is a single continuous trial
    hdr.nTrials     = 1; % it is a single continuous trial
    % keep the original details AND the list of channels as used by FieldTrip
    hdr.orig         = header_info;
    hdr.orig.chansel = chansel;
    % add the gradiometer definition
    hdr.grad         = itab2grad(header_info);

  case 'jaga16'
    % this is hard-coded for the Jinga-Hi JAGA16 system with 16 channels
    packetsize = (4*2 + 6*2 + 16*43*2); % in bytes
    % read the first packet
    fid  = fopen_or_error(filename, 'r');
    buf  = fread(fid, packetsize/2, 'uint16');
    fclose(fid);

    if buf(1)==0
      % it does not have timestamps, i.e. it is the raw UDP stream
      packetsize = packetsize - 8; % in bytes
      packet     = jaga16_packet(buf(1:(packetsize/2)), false);
    else
      % each packet starts with a timestamp
      packet = jaga16_packet(buf, true);
    end

    % determine the number of packets from the file size
    info     = dir(filename);
    npackets = floor((info.bytes)/packetsize/2);

    hdr             = [];
    hdr.Fs          = packet.fsample;
    hdr.nChans      = packet.nchan;
    hdr.nSamples    = 43;
    hdr.nSamplesPre = 0;
    hdr.nTrials     = npackets;
    hdr.label       = cell(hdr.nChans,1);
    hdr.chantype    = cell(hdr.nChans,1);
    hdr.chanunit    = cell(hdr.nChans,1);
    for i=1:hdr.nChans
      hdr.label{i} = sprintf('%d', i);
      hdr.chantype{i} = 'eeg';
      hdr.chanunit{i} = 'uV';
    end

    % store some low-level details
    hdr.orig.offset     = 0;
    hdr.orig.packetsize = packetsize;
    hdr.orig.packet     = packet;
    hdr.orig.info       = info;

  case {'manscan_mbi', 'manscan_mb2'}
    orig       = in_fopen_manscan(filename);
    hdr.Fs     = orig.prop.sfreq;
    hdr.nChans = numel(orig.channelmat.Channel);
    hdr.nTrials  = 1;
    if isfield(orig, 'epochs') && ~isempty(orig.epochs)
      hdr.nSamples = 0;
      for i = 1:numel(orig.epochs)
        hdr.nSamples =  hdr.nSamples + diff(orig.epochs(i).samples) + 1;
      end
    else
      hdr.nSamples = diff(orig.prop.samples) + 1;
    end
    if orig.prop.times(1) < 0
      hdr.nSamplesPre  = round(orig.prop.times(1)/hdr.Fs);
    else
      hdr.nSamplesPre  = 0;
    end
    for i=1:hdr.nChans
      hdr.label{i,1}    = orig.channelmat.Channel(i).Name;
      hdr.chantype{i,1} = lower(orig.channelmat.Channel(i).Type);
      if isequal(hdr.chantype{i,1}, 'eeg')
        hdr.chanunit{i, 1} = 'uV';
      else
        hdr.chanunit{i, 1} = 'unknown';
      end
    end
    hdr.orig = orig;

  case 'matlab'
    % read the header structure from a MATLAB file
    % it should either contain a "hdr" structure, or a FieldTrip data structure according to FT_DATATYPE_RAW
    w = whos(matfile(filename));
    if any(strcmp({w.name}, 'hdr'))
      hdr = loadvar(filename, 'hdr');
    elseif any(strcmp({w.name}, 'data')) || length(w)==1
      data = loadvar(filename, 'data');
      hdr = ft_fetch_header(data);
    end

  case 'mayo_mef30'
    ft_hastoolbox('mayo_mef', 1); % make sure mayo_mef exists
    hdr = read_mayo_mef30(filename, password);

  case 'mayo_mef21'
    ft_hastoolbox('mayo_mef', 1); % make sure mayo_mef exists
    hdr = read_mayo_mef21(filename, password);

  case 'mega_neurone'
    % ensure that this external toolbox is on the path
    ft_hastoolbox('neurone', 1);
    if filename(end)~=filesep
      % it should end with a slash
      filename = [filename filesep];
    end
    % this is like the EEGLAB data structure
    EEG = readneurone(filename);

    hdr.Fs          = EEG.srate;
    hdr.nChans      = EEG.nbchan;
    hdr.nSamples    = EEG.pnts;
    hdr.nSamplesPre = -EEG.xmin*EEG.srate;
    hdr.nTrials     = EEG.trials;
    try
      hdr.label       = { EEG.chanlocs.labels }';
    catch
      ft_warning('creating default channel names');
      for i=1:hdr.nChans
        hdr.label{i} = sprintf('chan%03d', i);
      end
    end
    ind = 1;
    for i = 1:length( EEG.chanlocs )
      if isfield(EEG.chanlocs(i), 'X') && ~isempty(EEG.chanlocs(i).X)
        hdr.elec.label{ind, 1} = EEG.chanlocs(i).labels;
        % this channel has a position
        hdr.elec.elecpos(ind,1) = EEG.chanlocs(i).X;
        hdr.elec.elecpos(ind,2) = EEG.chanlocs(i).Y;
        hdr.elec.elecpos(ind,3) = EEG.chanlocs(i).Z;
        ind = ind+1;
      end
    end

    if cache
      % also remember the data and events
      hdr.orig = EEG;
    else
      % remember only the header details
      hdr.orig = removefields(EEG, {'data', 'event'});
    end

  case 'micromed_trc'
    orig = read_micromed_trc(filename);
    hdr             = [];
    hdr.Fs          = orig.Rate_Min; % FIXME is this correct?
    hdr.nChans      = orig.Num_Chan;
    hdr.nSamples    = orig.Num_Samples;
    hdr.nSamplesPre = 0; % continuous
    hdr.nTrials     = 1; % continuous
    hdr.label       = cell(1,hdr.nChans);
    % give this warning only once
    hdr.label  = {orig.elec.Name};
    hdr.chanunit = {orig.elec.Unit};
    hdr.subjectname = orig.name;
    %warning('using a modified read_micromed_trc() function');

    % this should be a column vector
    hdr.label = hdr.label(:);
    % remember the original header details
    hdr.orig = orig;

  case {'mpi_ds', 'mpi_dap'}
    hdr = read_mpi_ds(filename);

  case 'netmeg'
    ft_hastoolbox('netcdf', 1);

    % this will read all NetCDF data from the file and subsequently convert
    % each of the three elements into a more easy to parse MATLAB structure
    s = netcdf(filename);

    for i=1:numel(s.AttArray)
      fname = fixname(s.AttArray(i).Str);
      fval  = s.AttArray(i).Val;
      if ischar(fval)
        fval = fval(fval~=0); % remove the \0 characters
        fval = strtrim(fval); % remove insignificant whitespace
      end
      Att.(fname) = fval;
    end

    for i=1:numel(s.VarArray)
      fname = fixname(s.VarArray(i).Str);
      fval  = s.VarArray(i).Data;
      if ischar(fval)
        fval = fval(fval~=0); % remove the \0 characters
        fval = strtrim(fval); % remove insignificant whitespace
      end
      Var.(fname) = fval;
    end

    for i=1:numel(s.DimArray)
      fname = fixname(s.DimArray(i).Str);
      fval  = s.DimArray(i).Dim;
      if ischar(fval)
        fval = fval(fval~=0); % remove the \0 characters
        fval = strtrim(fval); % remove insignificant whitespace
      end
      Dim.(fname) = fval;
    end

    % convert the relevant fields into the default header structure
    hdr.Fs          = 1000/Var.samplinginterval;
    hdr.nChans      = length(Var.channelstatus);
    hdr.nSamples    = Var.numsamples;
    hdr.nSamplesPre = 0;
    hdr.nTrials     = size(Var.waveforms, 1);
    hdr.chanunit    = cellstr(reshape(Var.channelunits, hdr.nChans, 2));
    hdr.chantype    = cellstr(reshape(lower(Var.channeltypes), hdr.nChans, 3));

    ft_warning('creating fake channel names');
    hdr.label = cell(hdr.nChans, 1);
    for i=1:hdr.nChans
      hdr.label{i} = sprintf('%d', i);
    end

    % remember the original details of the file
    % note that this also includes the data
    % this is large, but can be reused elsewhere
    hdr.orig.Att = Att;
    hdr.orig.Var = Var;
    hdr.orig.Dim = Dim;

    % construct the gradiometer structure from the complete header information
    hdr.grad = netmeg2grad(hdr);


  case 'nervus_eeg'
    hdr = read_nervus_header(filename);
    checkUniqueLabels = false;

  case 'neuralynx_dma'
    hdr = read_neuralynx_dma(filename);

  case 'neuralynx_sdma'
    hdr = read_neuralynx_sdma(filename);

  case 'neuralynx_ncs'
    ncs = read_neuralynx_ncs(filename, 1, 0);
    [p, f, x]       = fileparts(filename);
    hdr.Fs          = ncs.hdr.SamplingFrequency;
    hdr.label       = {f};
    hdr.nChans      = 1;
    hdr.nTrials     = 1;
    hdr.nSamplesPre = 0;
    hdr.nSamples    = ncs.NRecords * 512;
    hdr.orig        = ncs.hdr;
    FirstTimeStamp  = ncs.hdr.FirstTimeStamp;  % this is the first timestamp of the first block
    LastTimeStamp   = ncs.hdr.LastTimeStamp;   % this is the first timestamp of the last block, i.e. not the timestamp of the last sample
    hdr.TimeStampPerSample = double(LastTimeStamp - FirstTimeStamp) ./ ((ncs.NRecords-1)*512);
    hdr.FirstTimeStamp     = FirstTimeStamp;

  case 'neuralynx_nse'
    nse = read_neuralynx_nse(filename, 1, 0);
    [p, f, x]       = fileparts(filename);
    hdr.Fs          = nse.hdr.SamplingFrequency;
    hdr.label       = {f};
    hdr.nChans      = 1;
    hdr.nTrials     = nse.NRecords;  % each record contains one waveform
    hdr.nSamples    = 32;            % there are 32 samples in each waveform
    hdr.nSamplesPre = 0;
    hdr.orig        = nse.hdr;
    % FIXME add hdr.FirstTimeStamp and hdr.TimeStampPerSample

  case {'neuralynx_ttl', 'neuralynx_tsl', 'neuralynx_tsh'}
    % these are hardcoded, they contain an 8-byte header and int32 values for a single channel
    % FIXME this should be done similar as neuralynx_bin, i.e. move the hdr into the function
    hdr             = [];
    hdr.Fs          = 32556;
    hdr.nChans      = 1;
    hdr.nSamples    = (filesize(filename)-8)/4;
    hdr.nSamplesPre = 1;
    hdr.nTrials     = 1;
    hdr.label       = {headerformat((end-3):end)};

  case 'neuralynx_bin'
    hdr = read_neuralynx_bin(filename);

  case 'neuralynx_ds'
    hdr = read_neuralynx_ds(filename);

  case 'neuralynx_cds'
    hdr = read_neuralynx_cds(filename);

  case 'nexstim_nxe'
    hdr = read_nexstim_nxe(filename);

  case 'neuromag_headpos'
    % neuromag headposition file created with maxfilter, the position varies over time
    orig = read_neuromag_headpos(filename);
    hdr.Fs          = 1/(orig.data(1,2)-orig.data(1,1));
    hdr.nChans      = size(orig.data,1);
    hdr.nSamples    = size(orig.data,2);
    hdr.nSamplesPre = 0;   % convert from ms to samples
    hdr.nTrials     = 1;
    hdr.label       = orig.colheaders;

  case 'neuromag_maxfilterlog'
    % neuromag log file created with maxfilter
    log = read_neuromag_maxfilterlog(filename);
    hdr = [];
    hdr.label = {'t' 'e' 'g' 'v' 'r' 'd'};
    for i=1:numel(log.hpi)
      for j=1:11
        hdr.label{end+1} = sprintf('hpi%d_%02d', i, j);
      end
    end
    hdr.nChans = length(hdr.label);
    hdr.nSamples = length(log.t);
    hdr.nSamplesPre = 0;
    hdr.nTrials = 1;
    hdr.Fs = 1 / median(diff(log.t));
    hdr.orig = log;

  case {'neuromag_fif' 'neuromag_mne'}
    % ensure that the required low-level toolbox is available, if possible
    ft_hastoolbox('mne', 1);

    [fid, tree]  = fiff_open(filename);
    [info, meas] = fiff_read_meas_info(fid, tree);
    fclose(fid);

    % convert to FieldTrip format header
    hdr.label       = info.ch_names(:);
    hdr.nChans      = info.nchan;
    hdr.Fs          = info.sfreq;

    if isempty(coilaccuracy) && (ft_senstype(hdr, 'fieldline') || ft_senstype(hdr, 'quspin_neuro1'))
      ft_warning('OPM data requires a numeric value (0, 1, 2) for coilaccuracy');
      coilaccuracy = 0;
    end

    if ft_senstype(hdr, 'fieldline_v3')
      % default for FieldLine v3 is to remove the electronics chassis number from the channel names
      splitlabel = ft_getopt(varargin, 'splitlabel', true);
    end

    % allow FT_CHANTYPE to have a look in the original header details
    hdr.orig = info;
    hdr.chantype = ft_chantype(hdr);

    if ft_senstype(hdr, 'quspin_neuro1')
      sel = ismember(hdr.label, {'D0', 'D1', 'D2', 'D3', 'D4', 'D5', 'D6', 'D7', 'D8', 'D9', 'D10'});
      hdr.chantype(sel) = {'digital trigger'}; % somehow these are marked as kind=501=misc in the fif file
      sel = ismember(hdr.label, {'AI 0', 'AI 1', 'AI 2', 'AI 3', 'AI 4', 'AI 5', 'AI 6', 'AI 7', 'AI 8', 'AI 9', 'AI 10', 'AI 11', 'AI 12', 'AI 13', 'AI 14', 'AI 15'});
      hdr.chantype(sel) = {'analog trigger'}; % somehow these are marked as kind=3=other trigger in the fif file
    end

    % add a gradiometer structure for forward and inverse modelling
    try
      [grad, elec] = mne2grad(info, strcmp(coordsys, 'dewar'), coilaccuracy, coildeffile);
      if ~isempty(grad)
        hdr.grad = grad;
      end
      if ~isempty(elec)
        hdr.elec = elec;
      end
    catch
      disp(lasterr);
    end

    iscontinuous  = 0;
    isepoched     = 0;
    isaverage     = 0;
    if ~isempty(fiff_dir_tree_find(tree, FIFF.FIFFB_EVOKED)) % true file contains evoked responses
      isaverage = 1;

      try
        evoked_data    = fiff_read_evoked_all(filename);
        vartriallength = any(diff([evoked_data.evoked.first])) || any(diff([evoked_data.evoked.last]));
        if vartriallength
          % there are trials averages with variable durations in the file
          ft_warning('EVOKED FILE with VARIABLE TRIAL LENGTH! - check data have been processed accurately');
          hdr.nSamples = 0;
          for i=1:length(evoked_data.evoked)
            hdr.nSamples = hdr.nSamples + size(evoked_data.evoked(i).epochs, 2);
          end
          % represent it as a continuous file with a single trial
          % all trial average details will be available through read_event
          hdr.nSamplesPre = 0;
          hdr.nTrials     = 1;
          info.evoked     = evoked_data.evoked; % this is used by read_data to get the actual data, i.e. to prevent re-reading
          info.info       = evoked_data.info;   % keep all the details
          info.vartriallength = 1;
        else
          % represent it as a file with multiple trials, each trial has the same length
          % all trial average details will be available through read_event
          hdr.nSamples    = evoked_data.evoked(1).last - evoked_data.evoked(1).first + 1;
          hdr.nSamplesPre = -evoked_data.evoked(1).first;   % represented as negative number in fif file
          hdr.nTrials     = length(evoked_data.evoked);
          info.evoked     = evoked_data.evoked;             % this is used by read_data to get the actual data, i.e. to prevent re-reading
          info.info       = evoked_data.info;               % keep all the details
          info.vartriallength = 0;
        end

      catch
        % this happens if fiff_read_evoked_all cannot find evoked
        % responses, in which case it errors due to not assigning the
        % output variable "data"
        ft_warning('%s does not contain data', filename);
        hdr.nSamples    = 0;
        hdr.nSamplesPre = 0;
        hdr.nTrials     = 0;
      end

    elseif ~isempty(fiff_dir_tree_find(tree, FIFF.FIFFB_MNE_EPOCHS))
      isepoched = 1;

      % read in the epochs info, this also gets all the data already
      epochs = fiff_read_epochs(filename);
      epochs.data = permute(epochs.data, [2 3 1]); % chan x time x rpt

      hdr.nSamples    = length(epochs.times);
      hdr.nSamplesPre = sum(epochs.times < 0);
      hdr.nTrials     = size(epochs.data, 3); % because the data matrix has been permuted
      info.epochs     = epochs;  % this is used by read_data to get the actual data, i.e. to prevent re-reading

    else
      iscontinuous = 1;

      raw     = fiff_setup_read_raw(filename, 1);
      has_ias = ~isempty(fiff_dir_tree_find(meas,FIFF.FIFFB_IAS_RAW_DATA));

      if has_ias && istrue(checkmaxfilter)
        ft_error('Maxshield data should be corrected using Maxfilter prior to importing into FieldTrip, otherwise the data may be distorted. If you still want to proceed, consider setting the ''checkmaxfilter'' option to be ''no''.');
      elseif has_ias
        ft_warning('Maxshield data should be corrected using Maxfilter prior to importing into FieldTrip, otherwise the data may be distorted. If you still want to proceed, consider setting the ''checkmaxfilter'' option to be ''no''.');
      end
      hdr.nSamples    = raw.last_samp - raw.first_samp + 1; % number of samples per trial
      hdr.nSamplesPre = -raw.first_samp;
      % otherwise conflicts will occur in read_data
      hdr.nTrials     = 1;
      info.raw        = raw; % keep all the details
    end

    % remember the original header details
    hdr.orig = info;

    % these are useful to know in ft_read_event and ft_read_data
    hdr.orig.isaverage    = isaverage;
    hdr.orig.iscontinuous = iscontinuous;
    hdr.orig.isepoched    = isepoched;

  case 'neuromag_mex'
    % check that the required low-level toolbox is available
    ft_hastoolbox('meg-pd', 1);
    rawdata('any',filename);
    rawdata('goto', 0);
    megmodel('head',[0 0 0],filename);
    % get the available information from the fif file
    [orig.rawdata.range,orig.rawdata.calib]           = rawdata('range');
    [orig.rawdata.sf]                                 = rawdata('sf');
    [orig.rawdata.samples]                            = rawdata('samples');
    [orig.chaninfo.N,orig.chaninfo.S,orig.chaninfo.T] = chaninfo;           % Numbers, names & places
    [orig.chaninfo.TY,orig.chaninfo.NA]               = chaninfo('type');   % Coil type
    [orig.chaninfo.NO]                                = chaninfo('noise');  % Default noise level
    [orig.channames.NA,orig.channames.KI,orig.channames.NU] = channames(filename); % names, kind, logical numbers
    % read a single trial to determine the data size
    [buf, status] = rawdata('next');
    rawdata('close');

    % This is to solve a problem reported by Doug Davidson: The problem
    % is that rawdata('samples') is not returning the number of samples
    % correctly. It appears that the example script rawchannels in meg-pd
    % might work, however, so I want to use rawchannels to read in one
    % channel of data in order to get the number of samples in the file:
    if orig.rawdata.samples<0
      tmpchannel = 1;
      tmpvar = rawchannels(filename,tmpchannel);
      [orig.rawdata.samples] = size(tmpvar,2);
      clear tmpvar tmpchannel;
    end

    % convert to FieldTrip format header
    hdr.label       = orig.channames.NA;
    hdr.Fs          = orig.rawdata.sf;
    hdr.nSamplesPre = 0; % I don't know how to get this out of the file
    hdr.nChans      = size(buf,1);
    hdr.nSamples    = size(buf,2); % number of samples per trial
    hdr.nTrials     = orig.rawdata.samples ./ hdr.nSamples;
    % add a gradiometer structure for forward and inverse modelling
    hdr.grad = fif2grad(filename);
    % remember the original header details
    hdr.orig = orig;

  case 'neuroomega_mat'
    % These are MATLAB *.mat files created by the software 'Map File
    % Converter' from the proprietary .mpx files recorded by NeuroOmega

    %defining default if chantype is missing
    if isempty(chantype) || strcmpi(chantype{1},'chaninfo') || strcmpi(chantype{1},'info')
      chantype={'micro'};
    end

    chantype_dict={'micro','macro',     'analog', 'micro_lfp','macro_lfp','micro_hp','add_analog','emg', 'eeg', 'ecog';...
      'CRAW', 'CMacro_RAW','CANALOG', 'CLFP',     'CMacro_LFP',   'CSPK' ,'CADD_ANALOG','CEMG', 'CEEG', 'CECOG'};
    neuroomega_param={'_KHz','_KHz_Orig','_Gain','_BitResolution','_TimeBegin','_TimeEnd'};

    %identifying channels to be loaded
    orig = matfile(filename);
    fields_orig=who(orig);

    %is_param=endsWith(fields_orig,neuroomega_param); %Matlab 2017a
    is_param=zeros(length(fields_orig),1);
    for i=1:length(neuroomega_param)
      is_param = is_param | ~cellfun('isempty',regexp(fields_orig,strcat(neuroomega_param(i),'$'),'start'));
    end

    %creating channel info table
    channels_all=fields_orig(contains(fields_orig,chantype_dict(2,:)) & ~is_param);
    %Matching channels to chantypes
    M=cell2mat(cellfun(@(x) contains(channels_all,x),chantype_dict(2,:),'UniformOutput',false));
    chantype_ix = sum( cumprod(M == 0, 2), 2) + 1;
    Fs=cellfun(@(x) orig.([x '_KHz'])*1000,channels_all);
    chaninfo=table(channels_all,chantype_dict(1,chantype_ix)',Fs,'VariableNames',{'channel' 'chantype' 'Fs'});

    channels={}; channelstype={};
    for c = 1:length(chantype)
      chantype_dict_sel=strcmpi(chantype_dict(1,:),chantype{c});
      if sum(chantype_dict_sel)>0
        chanbasename=chantype_dict{2, chantype_dict_sel};
        sel_chan=fields_orig(strncmpi(fields_orig,chanbasename,length(chanbasename)) & ~is_param);
        if isempty(sel_chan)
          ft_warning(strjoin({'chantype ',chantype{c},' selected but no ',chanbasename,' found'}))
        else
          channels=[channels;sel_chan];
          channelstype=[channelstype;repmat(chantype(c),  size(sel_chan))];
        end
      else
        ft_warning(strjoin({'unknown chantype ',chantype{c}}));
      end
    end

    if ~isempty(channels)
      chan_t=table;
      for i=1:length(channels)
        ch=channels{i};
        ch_whos=whos(orig,ch);
        chan_t=[chan_t;{ch,orig.([ch,'_KHz'])*1000, orig.([ch,'_TimeBegin']), ch_whos.size(2)}];
      end
      chan_t.Properties.VariableNames={'channel' 'Fs' 'T0' 'nSamples'};

      Fs=unique(chan_t.Fs);
      T0=unique(chan_t.T0);
      nSamples=unique(chan_t.nSamples);

      if length(Fs)>1
        chan_t %; printing table for user
        ft_error('inconsistent channels with different sampling rates for %s',filename);
      end
      if length(T0)>1
        chan_t %; printing table for user
        ft_warning('inconsistent channels with different initial times for %s. Selecting minimum time',filename);
        T0 = min(T0);
      end
      if length(nSamples)>1
        chan_t %; printing table for user
        ft_warning('inconsistent number of samples across channels for %s. Selecting minimun nSample',filename)
        nSamples=min(nSamples);
      end
    else %If no channel selected
      Fs=nan; nSamples=nan; channelstype=chaninfo.chantype;
    end

    % building header
    hdr.Fs          = Fs;
    hdr.nChans      = length(channels);
    hdr.nSamples    = nSamples;
    hdr.nSamplesPre = 0;
    hdr.nTrials     = 1;
    hdr.label       = deblank(channels);
    hdr.chantype    = channelstype;
    hdr.chanunit    = repmat({'uV'},  size(hdr.label));
    % store the complete information in hdr.orig
    % ft_read_data and ft_read_event will get it from there
    hdr.orig        = [];
    hdr.orig.orig   = orig;
    hdr.orig.chaninfo = chaninfo;
    hdr.orig.fields = fields_orig;

  case 'neuroprax_eeg'
    orig = np_readfileinfo(filename);

    hdr.Fs          = orig.fa;
    hdr.nChans      = orig.K;
    hdr.nSamples    = orig.N;
    hdr.nSamplesPre = 0; % continuous
    hdr.nTrials     = 1; % continuous
    hdr.label       = orig.channels(:);
    hdr.unit        = orig.units(:);

    % remember the original header details
    hdr.orig = orig;

  case 'neuroscope_bin'
    [p,f,e]    = fileparts(filename);
    headerfile = fullfile(p,[f,'.xml']);
    hdr        = ft_read_header(headerfile, 'headerformat', 'neuroscope_xml');

  case 'neuroscope_ds'
    listing    = dir(filename);
    filenames  = {listing.name}';
    headerfile = filenames{~cellfun('isempty',strfind(filenames,'.xml'))};
    hdr        = ft_read_header(headerfile, 'headerformat', 'neuroscope_xml');

  case 'neuroscope_xml'
    ft_hastoolbox('neuroscope', 1);
    ft_hastoolbox('gifti', 1);

    % this pertains to generic header file, and the other neuroscope
    % formats will recurse into this one
    [p,f,e]    = fileparts(filename);
    if isempty(p), p = pwd; end
    listing    = dir(p);
    filenames  = {listing.name}';

    lfpfile_idx = find(~cellfun('isempty',strfind(filenames,'.eeg')));
    rawfile_idx = find(~cellfun('isempty',strfind(filenames,'.dat')));

    if ~isempty(lfpfile_idx)
      % FIXME this assumes only 1 such file, or at least it only takes the
      % first one.
      lfpfile = filenames{lfpfile_idx(1)};
    else
      lfpfile = {};
    end
    if ~isempty(rawfile_idx)
      rawfile = filenames{rawfile_idx(1)};
    end
    params     = LoadParameters(filename);

    hdr         = [];
    hdr.nChans  = params.nChannels;
    hdr.nTrials = 1; % is it always continuous? FIXME
    hdr.nSamplesPre = 0;

    if ~isempty(lfpfile)
      % use the sampling of the lfp-file to be leading
      hdr.Fs       = params.rates.lfp;
      hdr.nSamples = listing(strcmp(filenames,lfpfile)).bytes./(hdr.nChans*params.nBits/8);
      hdr.TimeStampPerSample = params.rates.wideband./params.rates.lfp;
    else
      % use the sampling of the raw-file to be leading
      hdr.Fs     = params.rates.wideband;
      hdr.nSamples = listing(strcmp(filenames,rawfile)).bytes./(hdr.nChans*params.nBits/8);
      hdr.TimeStampPerSample = 1;
    end
    hdr.orig = params;

    hdr.label = cell(hdr.nChans,1);
    for k = 1:hdr.nChans
      hdr.label{k} = ['chan',num2str(k,'%0.3d')];
    end

  case 'neurosim_evolution'
    hdr = read_neurosim_evolution(filename);

  case {'neurosim_ds' 'neurosim_signals'}
    hdr = read_neurosim_signals(filename);

  case 'neurosim_spikes'
    headerOnly = true;
    hdr = read_neurosim_spikes(filename, headerOnly);

  case 'nihonkohden_m00'
    % this is an ASCII file format which is rather inefficient to read
    if cache
      % read it once and store the data along with the header
      [hdr, dat] = read_nihonkohden_m00(filename);
      hdr.orig.dat = dat;
    else
      % read only the header
      hdr = read_nihonkohden_m00(filename);
    end

  case 'nihonkohden_eeg'
    ft_hastoolbox('brainstorm', 1);
    hdr = read_brainstorm_header(filename);

  case 'nimh_cortex'
    cortex = read_nimh_cortex(filename, 'epp', 'no', 'eog', 'no');
    % look at the first trial to determine whether it contains data in the EPP and EOG channels
    trial1  = read_nimh_cortex(filename, 'epp', 'yes', 'eog', 'yes', 'begtrial', 1, 'endtrial', 1);
    hasepp = ~isempty(trial1.epp);
    haseog = ~isempty(trial1.eog);
    if hasepp
      ft_warning('EPP channels are not yet supported');
    end
    % at the moment only the EOG channels are supported here
    if haseog
      hdr.label       = {'EOGx' 'EOGy'};
      hdr.nChans      = 2;
    else
      hdr.label       = {};
      hdr.nChans      = 0;
    end
    hdr.nTrials     = length(cortex);
    hdr.nSamples    = inf;
    hdr.nSamplesPre = 0;
    hdr.orig.trial = cortex;
    hdr.orig.hasepp = hasepp;
    hdr.orig.haseog = haseog;

  case 'ns_avg'
    orig = read_ns_hdr(filename);
    % do some reformatting/renaming of the header items
    hdr.Fs          = orig.rate;
    hdr.nSamples    = orig.npnt;
    hdr.nSamplesPre = round(-orig.rate*orig.xmin/1000);
    hdr.nChans      = orig.nchan;
    hdr.label       = orig.label(:);
    hdr.nTrials     = 1; % the number of trials in this datafile is only one, i.e. the average
    % remember the original header details
    hdr.orig = orig;

  case {'ns_cnt' 'ns_cnt16', 'ns_cnt32'}
    ft_hastoolbox('eeglab', 1);
    if strcmp(headerformat, 'ns_cnt')
      orig = loadcnt(filename); % let loadcnt figure it out
    elseif strcmp(headerformat, 'ns_cnt16')
      orig = loadcnt(filename, 'dataformat', 'int16');
    elseif strcmp(headerformat, 'ns_cnt32')
      orig = loadcnt(filename, 'dataformat', 'int32');
    end

    % do some reformatting/renaming of the header items
    hdr.Fs          = orig.header.rate;
    hdr.nChans      = orig.header.nchannels;
    hdr.nSamples    = orig.ldnsamples;
    hdr.nSamplesPre = 0;
    hdr.nTrials     = 1;
    for i=1:hdr.nChans
      hdr.label{i} = deblank(orig.electloc(i).lab);
    end
    % remember the original header details
    hdr.orig = orig;

  case 'ns_eeg'
    orig = read_ns_hdr(filename);
    % do some reformatting/renaming of the header items
    hdr.label       = orig.label;
    hdr.Fs          = orig.rate;
    hdr.nSamples    = orig.npnt;
    hdr.nSamplesPre = round(-orig.rate*orig.xmin/1000);
    hdr.nChans      = orig.nchan;
    hdr.nTrials     = orig.nsweeps;
    % remember the original header details
    hdr.orig = orig;

  case 'nmc_archive_k'
    hdr = read_nmc_archive_k_hdr(filename);

  case 'neuroshare' % NOTE: still under development
    % check that the required toolbox is available
    ft_hastoolbox('neuroshare', 1);
    tmp = read_neuroshare(filename);
    hdr.Fs          = tmp.hdr.analoginfo(end).SampleRate; % take the sampling freq from the last analog channel (assuming this is the same for all chans)
    hdr.nChans      = length(tmp.list.analog(tmp.analog.contcount~=0)); % get the analog channels, only the ones that are not empty
    hdr.nSamples    = max([tmp.hdr.entityinfo(tmp.list.analog).ItemCount]); % take the number of samples from the longest channel
    hdr.nSamplesPre = 0; % continuous data
    hdr.nTrials     = 1; % continuous data
    hdr.label       = {tmp.hdr.entityinfo(tmp.list.analog(tmp.analog.contcount~=0)).EntityLabel}; %%% contains non-unique chans?
    hdr.orig        = tmp; % remember the original header

  case 'nwb'
    % check that the required toolbox is available
    ft_hastoolbox('MatNWB', 1);
    tmp = nwbRead(filename); % is lazy, so should not be too costly
    es_key = tmp.searchFor('ElectricalSeries').keys; % find LFP or EEG data, which should be an ElectricalSeries object
    if isempty(es_key)
      error('Dataset does not contain an LFP or EEG signal (i.e., no object of the class ''ElectricalSeries'').')
    elseif numel(es_key) > 1 % && isempty(additional_user_input) % TODO: Try to sort this out with the user's help
      % Temporary fix: SpikeEventSeries is a daughter of ElectrialSeries but should not be found here (searchFor update on its way)
      es_key = es_key(contains(es_key,'lfp','IgnoreCase',true));
    end
    if numel(es_key) > 1 % in case we weren't able to sort out a single
      error('More than one ElectricalSeries present in data. Please specify which signal to use.')
    else
      eseries = io.resolvePath(tmp, es_key{1});
    end
    if isa(eseries.data, 'types.untyped.DataStub')
      hdr.nSamples = eseries.data.dims(2);
    elseif isa(eseries.data, 'types.untyped.DataPipe')
      hdr.nSamples = eseries.data.internal.maxSize(2);
    else
      warning('Cannot determine number of samples in the data.')
      hdr.nSamples = [];
    end
    hdr.Fs          = eseries.starting_time_rate;
    hdr.nSamplesPre = 0; % for now: hardcoded continuous data
    hdr.nTrials     = 1; % for now: hardcoded continuous data
    hdr.label       = {};
    tmp_ch          = io.resolvePath(tmp, eseries.electrodes.table.path).id.data.load; % electrode names
    for iCh=1:numel(tmp_ch) % TODO: does that work if nwb ids are strings?
      if isnumeric(tmp_ch(iCh))
        hdr.label(iCh,1) = {num2str(tmp_ch(iCh))};
      else
        hdr.label(iCh,1) = tmp_ch(iCh);
      end
    end
    hdr.nChans      = numel(hdr.label);
    [hdr.chanunit{1:hdr.nChans,1}] = deal(eseries.data_unit);
    hdr.chanunit    = strrep(hdr.chanunit, 'volt', 'V');
    hdr.chanunit    = strrep(hdr.chanunit, 'micro', 'u');
    % TODO: hdr.FirstTimeStamp
    % TODO: hdr.TimeStampPerSample

    % carry over some metadata
    hdr.orig        = [];
    fn = {'general_experimenter', ...
      'general_institution', ...
      'general_keywords', ...
      'general_lab', ...
      'general_notes', ...
      'general_related_publications', ...
      'general_session_id', ...
      'identifier', ...
      'session_description', ...
      'nwb_version', ...
      'help'};
    for iFn = 1:numel(fn)
      if isprop(tmp, fn{iFn}) && ~isempty(tmp.(fn{iFn}))
        hdr.orig.(fn{iFn}) = tmp.(fn{iFn});
      end
    end

  case 'artinis_oxy3'
    ft_hastoolbox('artinis', 1);
    hdr = read_artinis_oxy3(filename);

  case 'artinis_oxy4'
    ft_hastoolbox('artinis', 1);
    hdr = read_artinis_oxy4(filename);

  case 'artinis_oxy5'
    ft_hastoolbox('artinis', 1);
    hdr = read_artinis_oxy5(filename);

  case 'artinis_oxyproj'
    ft_hastoolbox('artinis', 1);
    hdr = read_oxyproj_header(filename);

  case 'plexon_ds'
    hdr = read_plexon_ds(filename);

  case 'plexon_ddt'
    orig = read_plexon_ddt(filename);
    hdr.nChans      = orig.NChannels;
    hdr.Fs          = orig.Freq;
    hdr.nSamples    = orig.NSamples;
    hdr.nSamplesPre = 0;      % continuous
    hdr.nTrials     = 1;      % continuous
    hdr.label       = cell(1,hdr.nChans);
    % give this warning only once
    ft_warning('creating fake channel names');
    for i=1:hdr.nChans
      hdr.label{i} = sprintf('%d', i);
    end
    % also remember the original header
    hdr.orig        = orig;

  case {'read_nex_data'} % this is an alternative reader for nex files
    orig = read_nex_header(filename);
    % assign the obligatory items to the output FCDC header
    numsmp = cell2mat({orig.varheader.numsmp});
    adindx = find(cell2mat({orig.varheader.typ})==5);
    if isempty(adindx)
      ft_error('file does not contain continuous channels');
    end
    hdr.nChans      = length(orig.varheader);
    hdr.Fs          = orig.varheader(adindx(1)).wfrequency;     % take the sampling frequency from the first A/D channel
    hdr.nSamples    = max(numsmp(adindx));                      % take the number of samples from the longest A/D channel
    hdr.nTrials     = 1;                                        % it can always be interpreted as continuous data
    hdr.nSamplesPre = 0;                                        % and therefore it is not trial based
    for i=1:hdr.nChans
      hdr.label{i} = deblank(char(orig.varheader(i).nam));
    end
    hdr.label = hdr.label(:);
    % also remember the original header details
    hdr.orig = orig;

  case {'plexon_nex' 'read_plexon_nex'} % this is the default reader for nex files
    orig = read_plexon_nex(filename);
    numsmp = cell2mat({orig.VarHeader.NPointsWave});
    adindx = find(cell2mat({orig.VarHeader.Type})==5);
    if isempty(adindx)
      ft_error('file does not contain continuous channels');
    end
    hdr.nChans      = length(orig.VarHeader);
    hdr.Fs          = orig.VarHeader(adindx(1)).WFrequency;     % take the sampling frequency from the first A/D channel
    hdr.nSamples    = max(numsmp(adindx));                      % take the number of samples from the longest A/D channel
    hdr.nTrials     = 1;                                        % it can always be interpreted as continuous data
    hdr.nSamplesPre = 0;                                        % and therefore it is not trial based
    for i=1:hdr.nChans
      hdr.label{i} = deblank(char(orig.VarHeader(i).Name));
    end
    hdr.label = hdr.label(:);
    hdr.FirstTimeStamp     = orig.FileHeader.Beg;
    hdr.TimeStampPerSample = orig.FileHeader.Frequency ./ hdr.Fs;
    % also remember the original header details
    hdr.orig = orig;

  case 'plexon_nex5' % this is the default reader for nex5 files
    orig = read_nex5(filename);
    numsmp = cell2mat({orig.VarHeader.NumberOfDataPoints});
    adindx = find(cell2mat({orig.VarHeader.Type})==5);
    if isempty(adindx)
      ft_error('file does not contain continuous channels');
    end
    % check that all continuous channels have the same sampling rate
    samplingRates = cell2mat({orig.VarHeader.WFrequency});
    contSamplingRates = samplingRates(adindx);
    if any(contSamplingRates~=contSamplingRates(1))
      ft_error('different sampling rates in continuous data not supported');
    end
    hdr.nChans      = length(orig.VarHeader);
    hdr.Fs          = orig.VarHeader(adindx(1)).WFrequency;    % take the sampling frequency from the first A/D channel
    hdr.TimeStampPerSample = orig.FileHeader.Frequency ./ hdr.Fs;
    % for hdr.nSamples, we need to calculate the last timestamp for every continuous channel
    maxTimestamp = 0;
    for i = 1:length(adindx)
      [nex, chanhdr] = read_nex5(filename, 'header', orig, 'channel', adindx(i), 'tsonly', 1);
      numPointsInLastFragment = numsmp(adindx(i)) - nex.indx(end) - 1;
      maxTimestamp = max(maxTimestamp, nex.ts(end)+hdr.TimeStampPerSample*(numPointsInLastFragment-1));
    end

    hdr.nSamples    = maxTimestamp/hdr.TimeStampPerSample;
    hdr.nTrials     = 1;                                        % it can always be interpreted as continuous data
    hdr.nSamplesPre = 0;                                        % and therefore it is not trial based
    for i=1:hdr.nChans
      hdr.label{i} = deblank(char(orig.VarHeader(i).Name));
    end
    hdr.label = hdr.label(:);
    hdr.FirstTimeStamp     = orig.FileHeader.Beg;
    hdr.TimeStampPerSample = orig.FileHeader.Frequency ./ hdr.Fs;
    % also remember the original header details
    hdr.orig = orig;

  case 'plexon_plx'
    orig = read_plexon_plx(filename);
    if orig.NumSlowChannels==0
      ft_error('file does not contain continuous channels');
    end
    fsample = [orig.SlowChannelHeader.ADFreq];
    if any(fsample~=fsample(1))
      ft_error('different sampling rates in continuous data not supported');
    end
    for i=1:length(orig.SlowChannelHeader)
      label{i} = deblank(orig.SlowChannelHeader(i).Name);
    end
    % continuous channels don't always contain data, remove the empty ones
    sel  = [orig.DataBlockHeader.Type]==5;  % continuous
    chan = [orig.DataBlockHeader.Channel];
    for i=1:length(label)
      chansel(i) = any(chan(sel)==orig.SlowChannelHeader(i).Channel);
    end
    chansel = find(chansel); % this is required for timestamp selection
    label = label(chansel);
    % only the continuous channels are returned as visible
    hdr.nChans      = length(label);
    hdr.Fs          = fsample(1);
    hdr.label       = label;
    % also remember the original header
    hdr.orig        = orig;

    % select the first continuous channel that has data
    sel = ([orig.DataBlockHeader.Type]==5 & [orig.DataBlockHeader.Channel]==orig.SlowChannelHeader(chansel(1)).Channel);
    % get the timestamps that correspond with the continuous data
    tsl = [orig.DataBlockHeader(sel).TimeStamp]';
    tsh = [orig.DataBlockHeader(sel).UpperByteOf5ByteTimestamp]';
    ts  = timestamp_plexon(tsl, tsh);  % use helper function, this returns an uint64 array

    % determine the number of samples in the continuous channels
    num = [orig.DataBlockHeader(sel).NumberOfWordsInWaveform];
    hdr.nSamples    = sum(num);
    hdr.nSamplesPre = 0;      % continuous
    hdr.nTrials     = 1;      % continuous

    % the timestamps indicate the beginning of each block, hence the timestamp of the last block corresponds with the end of the previous block
    hdr.TimeStampPerSample = double(ts(end)-ts(1))/sum(num(1:(end-1)));
    hdr.FirstTimeStamp     = ts(1);                                                %  the timestamp of the first continuous sample

    % also make the spike channels visible
    for i=1:length(orig.ChannelHeader)
      hdr.label{end+1} = deblank(orig.ChannelHeader(i).Name);
    end
    hdr.label = hdr.label(:);
    hdr.nChans = length(hdr.label);

  case {'ricoh_ave', 'ricoh_con', 'ricoh_mrk'}
    % header can be read with Ricoh MEG Reader
    hdr = read_ricoh_header(filename);
    % add a gradiometer structure for forward and inverse modelling
    hdr.grad = ricoh2grad(hdr);
    hdr.chantype = ft_chantype(hdr.label);
    unk = find(strcmp('unknown', hdr.chantype));
    %  Warning message:
    if ~isempty(unk)
      label_unk = hdr.label(unk);
      no_unk = num2cell(unk);
      C = [label_unk(:), no_unk(:)] .';
      ft_warning(['Unknown channel types: (label, no) =' repmat('( %s, %d ) ', 1, length(unk) ) '\n'], C{:})
    end

  case 'smi_txt'
    smi = read_smi_txt(filename);
    hdr.nChans              = size(smi.dat,1);
    hdr.nSamples            = size(smi.dat,2);
    hdr.nSamplesPre         = 0;
    hdr.nTrials             = 1;

    hdr.label               = smi.label;
    hdr.Fs                  = smi.Fs;
    hdr.FirstTimeStamp      = smi.timestamp(1);
    hdr.TimeStampPerSample  = mean(diff(smi.timestamp)); % these timestamps are in microseconds

    if cache
      % remember all header and data details upon request
      hdr.orig = smi;
    else
      % remember only the original header details
      hdr.orig.header = smi.header;
    end

    % add channel units when possible.
    for i=1:hdr.nChans
      chanunit = regexp(hdr.label{i,1},'(?<=\[).+?(?=\])','match');
      if ~isempty(chanunit)
        hdr.chanunit{i,1} = chanunit{1};
        hdr.chantype{i,1} = 'eyetracker';
      else
        hdr.chanunit{i,1} = 'unknown';
        hdr.chantype{i,1} = 'unknown';
      end
    end

  case 'tmsi_poly5'
    orig = read_tmsi_poly5(filename);
    % the header contains all channels twice (for the low and high data word)
    % it seems that the file format was designed for 16 bit, and only later extended to 32 bit
    hdr             = [];
    hdr.nChans      = orig.header.NumberOfSignals/2;
    hdr.Fs          = orig.header.FS;
    hdr.nSamples    = orig.header.NumberSampleBlocks * orig.header.SamplePeriodsPerBlock;
    hdr.nSamplesPre = 0;
    hdr.nTrials     = 1; % continuous
    for i=2:2:orig.header.NumberOfSignals
      % remove the '(Lo) ' and the '(Hi) ' section
      hdr.label{i/2} = strtrim(orig.description(i).SignalName(6:end)');
    end
    % determine the EEG channels
    iseeg = true(size(hdr.label));
    iseeg = iseeg & cellfun(@isempty, regexp(hdr.label, 'BIP.*'));
    iseeg = iseeg & cellfun(@isempty, regexp(hdr.label, 'AUX.*'));
    iseeg = iseeg & cellfun(@isempty, regexp(hdr.label, 'Digi.*'));
    iseeg = iseeg & cellfun(@isempty, regexp(hdr.label, 'Saw.*'));
    iseeg = iseeg & cellfun(@isempty, regexp(hdr.label, 'Bit.*'));
    istrg = ~cellfun(@isempty, regexp(hdr.label, 'Digi.*'));
    hdr.chanunit = cell(size(hdr.label));
    hdr.chantype = cell(size(hdr.label));
    hdr.chanunit(:) = {'unknown'};
    hdr.chantype(:) = {'unknown'};
    hdr.chanunit(iseeg) = {'uV'};
    hdr.chantype(iseeg) = {'eeg'};
    hdr.chantype(istrg) = {'trigger'};
    % remember the original header details
    hdr.orig = orig;

  case 'tobii_tsv'
    tsv = read_tobii_tsv(filename);
    % keyboard
    % remember the original header details
    hdr.orig = tsv;

  case {'tdt_tsq', 'tdt_tev'}
    % FIXME the code below is not yet functional, it requires more input from the ESI in Frankfurt
    %     tsq = read_tdt_tsq(headerfile);
    %     k = 0;
    %     chan = unique([tsq.channel]);
    %     % loop over the physical channels
    %     for i=1:length(chan)
    %       chansel = [tsq.channel]==chan(i);
    %       code = unique({tsq(chansel).code});
    %       % loop over the logical channels
    %       for j=1:length(code)
    %         codesel = false(size(tsq));
    %         for k=1:numel(codesel)
    %           codesel(k) = isequal(tsq(k).code, code{j});
    %         end
    %         % find the first instance of this logical channel
    %         this = find(chansel(:) & codesel(:), 1);
    %         % add it to the list of channels
    %         k = k + 1;
    %         frequency(k) = tsq(this).frequency;
    %         label{k}     = [char(typecast(tsq(this).code, 'uint8')) num2str(tsq(this).channel)];
    %         tsqorig(k)   = tsq(this);
    %       end
    %     end
    ft_error('not yet implemented');

  case {'yokogawa_ave', 'yokogawa_con', 'yokogawa_raw', 'yokogawa_mrk'}
    % header can be read with two toolboxes: Yokogawa MEG Reader and Yokogawa MEG160 (old inofficial toolbox)
    % newest toolbox takes precedence.
    if ft_hastoolbox('yokogawa_meg_reader', 3) % stay silent if it cannot be added
      hdr = read_yokogawa_header_new(filename);
      % add a gradiometer structure for forward and inverse modelling
      hdr.grad = yokogawa2grad_new(hdr);
      hdr.chantype = ft_chantype(hdr.label);
      unk = find(strcmp('unknown', hdr.chantype));
      %  Warning message:
      if ~isempty(unk)
        label_unk = hdr.label(unk);
        no_unk = num2cell(unk);
        C = [label_unk(:), no_unk(:)] .';
        ft_warning(['Unknown channel types: (label, no) =' repmat('( %s, %d ) ', 1, length(unk) ) '\n'], C{:})
      end
    else
      ft_hastoolbox('yokogawa', 1); % try it with the old version of the toolbox
      hdr = read_yokogawa_header(filename);
      % add a gradiometer structure for forward and inverse modelling
      hdr.grad = yokogawa2grad(hdr);
    end


  case {'audio_wav', 'audio_ogg', 'audio_flac', 'audio_au', 'audio_aiff', 'audio_aif', 'audio_aifc', 'audio_mp3', 'audio_m4a', 'audio_mp4'}
    % prior to MATLAB R2015b this used to be done with "wavread"
    % but the audioinfo/audioread function are at least available from 2012b up
    info = audioinfo(filename);
    hdr.Fs          = info.SampleRate;
    hdr.nChans      = info.NumChannels;
    hdr.nSamples    = info.TotalSamples;
    hdr.nSamplesPre = 0;
    hdr.nTrials     = 1;
    [p, f, x] = fileparts(filename);
    if hdr.nChans>1
      ft_warning('creating fake channel names');
      for i=1:hdr.nChans
        % use the file name and channel number
        hdr.label{i,1} = sprintf('%d', i);
        hdr.chantype{i,1} = 'audio';
      end
    else
      % use the filename as the channel name
      hdr.label{1,1} = f;
      hdr.chantype{1,1} = 'audio';
    end
    % remember the details
    hdr.orig = info;

  case 'videomeg_aud'
    hdr = read_videomeg_aud(filename);

  case 'videomeg_vid'
    hdr = read_videomeg_vid(filename);
    checkUniqueLabels = false;

  case 'video'
    hdr = read_video(filename);
    checkUniqueLabels = false;

  case 'yorkinstruments_hdf5'
    orig            = read_yorkinstruments_hdf5_meta(filename);
    hdr.Fs          = orig.SampleFrequency;
    hdr.nChans      = orig.NChannels;
    hdr.nSamples    = orig.NSamples;
    hdr.nSamplesPre = 0;    % No YI epoched data type
    hdr.nTrials = 1;
    hdr.label       = cellstr(orig.ChNames);
    hdr.chantype    = cellstr(orig.ChType);
    hdr.chanunit    = cellstr(orig.ChUnit);
    % remember original header details
    hdr.orig        = orig;

  otherwise
    if exist(headerformat, 'file')
      % attempt to run "headerformat" as a function, this allows the user to specify an external reading function
      % this is also used for bids_tsv, biopac_acq, motion_c3d, opensignals_txt, qualisys_tsv, sccn_xdf, and possibly others
      hdr = feval(headerformat, filename);
    elseif strcmp(fallback, 'biosig') && ft_hastoolbox('BIOSIG', 1)
      try
        % there is no guarantee that biosig can read it
        hdr = read_biosig_header(filename);
      catch
        ft_error('unsupported header format "%s"', headerformat);
      end
    else
      ft_error('unsupported header format "%s"', headerformat);
    end

end % switch headerformat


% Sometimes, the not all labels are correctly filled in by low-level reading functions. See for example bug #1572.
% First, make sure that there are enough (potentially empty) labels:
if numel(hdr.label) < hdr.nChans
  ft_warning('low-level reading function did not supply enough channel labels');
  hdr.label{hdr.nChans} = [];
end
% Now, replace all empty labels with new name:
if any(cellfun(@isempty, hdr.label))
  ft_warning('channel labels should not be empty, creating unique labels');
  hdr.label = fixlabels(hdr.label);
end

if checkUniqueLabels
  if length(hdr.label)~=length(unique(hdr.label))
    % all channels must have unique names
    ft_warning('all channels must have unique labels, creating unique labels');
    megflag = ft_chantype(hdr, 'meg');
    eegflag = ft_chantype(hdr, 'eeg');
    for i=1:hdr.nChans
      sel = find(strcmp(hdr.label{i}, hdr.label));
      if length(sel)>1
        % renaming the first instance is particularly disruptive when the channels are
        % part of standard MEG or EEG channel set, so that should be avoided
        if any(megflag(sel))
          sel = setdiff(sel, sel(find(megflag(sel), 1)));
        elseif any(eegflag(sel))
          sel = setdiff(sel, sel(find(eegflag(sel), 1)));
        else
          sel = sel(2:end);
        end
        for j=1:length(sel)
          % add a number to the original channel name
          hdr.label{sel(j)} = sprintf('%s-%d', hdr.label{sel(j)}, j);
        end
      end
    end
  end
end

% as of November 2011, the header is supposed to include the channel type (see FT_CHANTYPE,
% e.g. meggrad, megref, eeg) and the units of each channel (see FT_CHANUNIT, e.g. uV, fT)

% ensure that the output grad is according to the latest definition
if isfield(hdr, 'grad')
  hdr.grad = ft_datatype_sens(hdr.grad);
end

% ensure that the output elec is according to the latest definition
if isfield(hdr, 'elec')
  hdr.elec = ft_datatype_sens(hdr.elec);
end

% ensure that the output opto is according to the latest definition
if isfield(hdr, 'opto')
  try
    hdr.opto = ft_datatype_sens(hdr.opto);
  catch
    % the NIRS optode structure is incomplete when reading/converting it from Homer files
    ft_warning('optode structure is not compliant with FT_DATATYPE_SENS');
  end
end

if ~isfield(hdr, 'chantype') && checkUniqueLabels
  % use a helper function which has some built in intelligence
  hdr.chantype = ft_chantype(hdr);
end

if ~isfield(hdr, 'chanunit') && checkUniqueLabels
  % use a helper function which has some built in intelligence
  hdr.chanunit = ft_chanunit(hdr);
end

if (strcmp(readbids, 'yes') || strcmp(readbids, 'ifmakessense')) && isbids
  % the BIDS sidecar files extend/overrule the information that is present in the file header itself
  try
    if exist('data_json', 'var')
      hdr.Fs = data_json.SamplingFrequency;
    end
    if exist('channels_tsv', 'var')
      assert(length(channels_tsv.name)  == hdr.nChans, 'number of channels is not consistent with the BIDS channels.tsv');
      assert(length(channels_tsv.type)  == hdr.nChans, 'number of channels is not consistent with the BIDS channels.tsv');
      assert(length(channels_tsv.units) == hdr.nChans, 'number of channels is not consistent with the BIDS channels.tsv');
      hdr.label    = channels_tsv.name;
      hdr.chantype    = lower(channels_tsv.type);
      hdr.chanunit = channels_tsv.units;
    end
    if exist('electrodes_tsv', 'var')
      hdr.elec         = [];
      hdr.elec.label   = electrodes_tsv.name;
      hdr.elec.elecpos = [electrodes_tsv.x electrodes_tsv.y electrodes_tsv.z];
    end
    if exist('optodes_tsv', 'var')
      hdr.opto           = [];
      hdr.opto.optolabel = optodes_tsv.name;
      hdr.opto.optopos   = [optodes_tsv.x optodes_tsv.y optodes_tsv.z];
      if all(all(isnan(hdr.opto.optopos)))
        try
          hdr.opto.optopos = [optodes_tsv.template_x optodes_tsv.template_y optodes_tsv.template_z];
        end
      end
      hdr.opto.optotype = optodes_tsv.type;
      hdr.opto.wavelength = unique(channels_tsv.wavelength_nominal)';
      if exist('channels_tsv', 'var')
        hdr.opto.label = channels_tsv.name;
        if exist('coordsystem_json', 'var') && ~isempty(coordsystem_json)
          hdr.opto.unit = coordsystem_json.NIRSCoordinateUnits;
        end
        M = height(channels_tsv);
        N = height(optodes_tsv);
        hdr.opto.tra = zeros(M, N);
        for i=1:M
          tx = find(strcmp(optodes_tsv.name, channels_tsv.source(i)));
          rx = find(strcmp(optodes_tsv.name, channels_tsv.detector(i)));
          hdr.opto.tra(i, tx) = +find(channels_tsv.wavelength_nominal(i) == hdr.opto.wavelength);
          hdr.opto.tra(i, rx) = -find(channels_tsv.wavelength_nominal(i) == hdr.opto.wavelength);
        end
      end
    end
    if exist('coordsystem_json', 'var') && ~isempty(coordsystem_json)
      if isfield(hdr, 'grad')
        if strcmp(coordsys, 'dewar')
          % the sensors will be in dewar coordinates, regardless of the coordsystem_json
        else
          % the coordsystem_json overrules the coordinate system
          hdr.grad.coordsys = coordsystem_json.MEGCoordinateSystem;
        end
        % the units of the grad structure will be correct, they differ between MEG systems, and may depend on coilaccuracy
        % convert them to the units specified in coordsystem_json
        hdr.grad = ft_convert_units(hdr.grad, coordsystem_json.MEGCoordinateUnits);
      end
      if isfield(hdr, 'elec')
        hdr.elec.coordsys = coordsystem_json.EEGCoordinateSystem;
        hdr.elec.unit     = coordsystem_json.EEGCoordinateUnits;
      end
      if isfield(hdr, 'opto')
        hdr.opto.coordsys = coordsystem_json.NIRSCoordinateSystem;
        hdr.opto.unit     = coordsystem_json.NIRSCoordinateUnits;
      end
    end
  catch ME
    if strcmp(readbids, 'yes')
      rethrow(ME);
    else
      ft_warning(ME.message);
    end
  end % catch errors
end % if readbids and isbids

% ensure that these are column arrays and that they do not have empty entries
hdr.label = fixlabels(hdr.label);
if isfield(hdr, 'chantype'), hdr.chantype = fixchantype(hdr.chantype); end
if isfield(hdr, 'chanunit'), hdr.chanunit = fixchanunit(hdr.chanunit); end

% ensure that these are double precision and not integers, otherwise
% subsequent computations that depend on these might be messed up
hdr.Fs          = double(hdr.Fs);
hdr.nSamples    = double(hdr.nSamples);
hdr.nSamplesPre = double(hdr.nSamplesPre);
hdr.nTrials     = double(hdr.nTrials);
hdr.nChans      = double(hdr.nChans);

if ~isempty(splitlabel) && istrue(splitlabel)
  % this is default for CTF and FieldLine v3
  hdr.label = strtok(hdr.label, '-');
  % also update the grad, elec and opto structure
  if isfield(hdr, 'grad')
    hdr.grad.label = strtok(hdr.grad.label, '-');
    if isfield(hdr.grad, 'balance')
      % also update the G1BR, G2BR and G3BR balancing structures
      fn = fieldnames(hdr.grad.balance);
      for i=1:numel(fn)
        if isstruct(hdr.grad.balance.(fn{i}))
          hdr.grad.balance.(fn{i}).labelold = strtok(hdr.grad.balance.(fn{i}).labelold, '-');
          hdr.grad.balance.(fn{i}).labelnew = strtok(hdr.grad.balance.(fn{i}).labelnew, '-');
        end
      end
    end
  end
  if isfield(hdr, 'elec')
    hdr.elec.label = strtok(hdr.elec.label, '-');
  end
  if isfield(hdr, 'opto')
    hdr.opto.label = strtok(hdr.opto.label, '-');
  end
end

if inflated
  % compressed file has been unzipped on the fly, clean up
  if strcmp(headerformat, 'brainvision_vhdr')
    % don't delete the header file yet, ft_read_data might still need it
    % the files will be cleaned up by ft_read_data
  else
    delete(filename);
  end
end

if cache && exist(headerfile, 'file')
  % put the header in the cache
  cacheheader = hdr;
  % update the header details (including time stamp, size and name)
  cacheheader.details = dir(headerfile);
  % fprintf('added header to cache\n');
end


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% SUBFUNCTION to determine the file size in bytes
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [siz] = filesize(filename)
l = dir(filename);
if l.isdir
  ft_error('"%s" is not a file', filename);
end
siz = l.bytes;

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% SUBFUNCTION to determine the file size in bytes
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [hdr] = recursive_read_header(filename)
[p, f, x] = fileparts(filename);
ls = dir(filename);
ls = ls(~strcmp({ls.name}, '.'));  % exclude this directory
ls = ls(~strcmp({ls.name}, '..')); % exclude parent directory
for i=1:length(ls)
  % make sure that the directory listing includes the complete path
  ls(i).name = fullfile(filename, ls(i).name);
end
lst = {ls.name};
hdr = cell(size(lst));
sel = zeros(size(lst));
for i=1:length(lst)
  % read the header of each individual file
  try
    thishdr = ft_read_header(lst{i});
    if isstruct(thishdr)
      thishdr.filename = lst{i};
    end
  catch
    thishdr = [];
    ft_warning(lasterr);
    fprintf('while reading %s\n\n', lst{i});
  end
  if ~isempty(thishdr)
    hdr{i} = thishdr;
    sel(i) = true;
  else
    sel(i) = false;
  end
end
sel = logical(sel(:));
hdr = hdr(sel);
tmp = {};
for i=1:length(hdr)
  if isstruct(hdr{i})
    tmp = cat(1, tmp, hdr(i));
  elseif iscell(hdr{i})
    tmp = cat(1, tmp, hdr{i}{:});
  end
end
hdr = tmp;

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% SUBFUNCTION to fill in empty labels
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function labels = fixlabels(labels)
if isnumeric(labels)
  % convert the array of numbers into the corresponding strings
  labels = cellfun(@num2str, num2cell(labels), 'UniformOutput', false);
end
for i = find(cellfun(@isempty, {labels{:}}))
  labels{i} = sprintf('%d', i);
end
labels = labels(:);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% SUBFUNCTION to fill in empty chantype
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function chantype = fixchantype(chantype)
if isnumeric(chantype)
  % convert the array of numbers into the corresponding strings
  chantype = cellfun(@num2str, num2cell(chantype), 'UniformOutput', false);
end
sel = cellfun(@isempty, chantype) | strcmp(chantype, 'NaN');
chantype(sel) = {'unknown'};
chantype = chantype(:);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% SUBFUNCTION to fill in empty chanunit
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function chanunit = fixchanunit(chanunit)
if isnumeric(chanunit)
  % convert the array of numbers into the corresponding strings
  chanunit = cellfun(@num2str, num2cell(chanunit), 'UniformOutput', false);
end
sel = cellfun(@isempty, chanunit) | strcmp(chanunit, 'NaN');
chanunit(sel) = {'unknown'};
chanunit = chanunit(:);
