Using the classification module together with FieldTrip data

This example demonstrates how to use neuroimaging data obtained from FieldTrip together with the classification module. Here, we have more control over what will happen compared with statistics_crossvalidate. In the example, we make use of covert attention data of one subject that has already been frequency analyzed.

The data consists of 7 different frequencies at 274 channels at time points [-0.5 0 0.5 1 1.5 2 2.5]. We can expect evoked response after the cue and alpha modulation after about 1 second.

Simple classification examples

Simple classification examples

function classification_demo()

get data and design (class labels)

[data,design] = read_imaging_data();

selected 38 channels
selected 5 time bins
selected 7 frequency bins
Warning: PACK can only be used from the MATLAB command line.

specify classification procedure

myproc = clfproc({ ...
    standardizer() ...
    lr() ...
    });

validation method; randomize trial order and give verbose output

cv = crossvalidator('procedure',myproc,'cvfolds',0.8,'randomize',true,'verbose',true);

compute crossvalidation results

cv = cv.validate(data,design);

collapsing data
shuffling data
validating using 20% of the data for testing
 Iteration   FunEvals     Step Length    Function Val        Opt Cond
         1          2     4.45469e-05     1.29121e+02     1.97613e+04
         2          3     5.06014e-01     8.80241e+01     1.40043e+04
         3          4     7.14013e-01     5.82710e+01     1.50823e+04
         4          5     6.62987e-01     4.33959e+01     9.20018e+03
         5          6     1.00000e+00     2.55110e+01     3.27892e+03
         6          7     1.00000e+00     1.54855e+01     2.13604e+03
         7          8     1.00000e+00     6.44725e+00     9.28427e+02
         8          9     1.00000e+00     2.89214e+00     5.54140e+02
         9         10     1.00000e+00     1.31877e+00     3.02743e+02
        10         11     1.00000e+00     6.60184e-01     1.14510e+02
        11         12     1.00000e+00     3.37352e-01     6.07519e+01
        12         13     1.00000e+00     1.63936e-01     3.23591e+01
        13         14     1.00000e+00     7.48559e-02     1.45611e+01
        14         15     1.00000e+00     3.84171e-02     7.29850e+00
        15         16     1.00000e+00     1.78983e-02     3.64549e+00
        16         17     1.00000e+00     8.54369e-03     1.71453e+00
        17         18     1.00000e+00     4.14930e-03     9.73336e-01
        18         19     1.00000e+00     1.86230e-03     3.68013e-01
        19         20     1.00000e+00     9.89266e-04     1.90982e-01
        20         21     1.00000e+00     4.71974e-04     1.00633e-01
        21         22     1.00000e+00     2.23314e-04     4.48458e-02
        22         23     1.00000e+00     1.12793e-04     2.19662e-02
        23         24     1.00000e+00     5.51808e-05     1.12083e-02
        24         25     1.00000e+00     2.61895e-05     5.26937e-03
        25         26     1.00000e+00     1.25908e-05     2.54077e-03
        26         27     1.00000e+00     6.26867e-06     1.20276e-03
Function Value changing by less than TolFun

get average classification accuracy

cv.evaluate('metric','accuracy')

ans =

   0.840000000000000

significance compared with a baseline classifier

cv.significance

performing one-sided biniomial test (p=0.05)
null hypothesis rejected (0.00137428<0.05);
significant difference from majority classification (class 1 with prior of 0.5)

ans =

   0.001374275875832

for this classifier we can project parameters back onto the scalp

% get the structure
load ~/code/classification/toolboxes/bayesbrain/examples/freqli;

% change dimensions
freqLI.time = 1;
freqLI.freq = [5 8 12 20 30 50 70];
freqLI.time = 0.5:0.5:2.5;
freqLI.powspctrm = zeros(274,7,5);
freqLI.dimord = 'chan_freq_time';

% set powerspectrum to parameters
channels = ismember(freqLI.label,channelselection({'MLO' 'MRO'},freqLI.label));
freqLI.powspctrm(channels,:,:) = reshape(full(cv.procedure.clfmethods{2}.model(1,1:(end-1))),size(freqLI.powspctrm(channels,:,:)));

% predictive map
tcfg.layout = 'CTF274.lay';
topoplotTFR(tcfg,freqLI);

reading layout from file CTF274.lay

Using the classification module together with FieldTrip data

Contents

Simple classification examples