usage: rois = getInstances(v,rois,stimvol)
purpose: compute instances by taking mean after stimulus occurrences

N.B. This routine returns slightly different instances from the ones returned by svmclassify. One big difference is that svmclassify subtracts out the block mean (in getInstances, we don't do that and assume that the filtering takes care of this for us). svmclassify may also require that all instance types have the same number of repeats - I didn't check that carefully, but it seems to be the case since it returns fewer numbers of instances than getInstances.

usage:

rois = getSortIndex(v,rois,r2)
rois = getSortIndex(v,rois,r2Left,r2Right)

purpose: adds a sort index to the rois based on the r2 values passed in. If two r2 matrices are passed in then this is a split design with different sort values for ROIs that start with 'l' or start with 'r' the first r2 matrix will be used for ROIs that start with 'l' and the second will be used for ROIs that start with 'r'. The r2 matrices are of the dimensions of the scan.

usage: leaveOneOut(instances)
purpose: Do leaveOneOut cross-validated classification on the passed in instances. Instances is a cell array which has a kxn matrix for each class, where k = number of repetitions and n = number of dimensions (e.g. num voxels).

Can also be called on ROIS that have instances computed.

rois = getInstances(v,rois,stimvol);
rois = leaveOneOut(rois);

Return structure (rois.classify.leaveOneOut) contains the following fields

usage: classifier = buildClassifier(instances,varargin)
purpose: Creates a classifier for later classification.

usage: [class classifierOut] = classifyInstance(classifier,instance)
purpose: classify a novel instance using the classifier returned by buildClassifier.

Description : perform multi-class classification as in Kamitani and Tong paper. A classifier is trained for each pair of classes. A given classifier projects each test instance on the hyperplane orthogonal to the classifier's decision boundary. For each test instance, a pair of class is thus associated with a projection value. To get an idea of which class the test instance is more likely to belong to, one can sum the projection values of all pairs of class that include the class. the test instance belongs to the class with the highest sum of projection value. Note that this is an ad hoc and not analytically proven solution. (steeve: I wrote that and that seriously needs rewriting !)

usage: combined = computeCombinedCorrect(v,rois,stimvol1,stimvol2,<startLag>,<blockLen>)
purpose: Computes the combined correct across hemispheres for l/r split designs.

usage: displayCombinedCorrect(v,combined,<combinedTitle>,saveEPS)
purpose: Computes the combined correct across hemispheres for l/r split designs.

usage: [weightMaps weightMapNames] = getClassifierWeightMap(v,rois,stimvol);
purpose: returns classifier weight maps that can be installed in MLR with mrDispOverlay

mrQuit;
v = newView;
rois = loadROITSeries(v,{'l_mt','r_mt'});
stimvol = getStimvol(v,'localdir');
[weightMaps weightMapNames] = getClassifierWeightMap(v,rois,stimvol);
v = loadAnat(v,'jg_left_occipital.hdr');
mrDispOverlay(weightMaps,rois{1}.scanNum,rois{1}.groupNum,v,weightMapNames,'colormapType','normal','cmap',splitcmap,'range=[-1 1]','clip=[-1 1]');
overlay = refreshMLRDisplay(viewGet(v,'viewNum'));

usage: rois = stackROIs(rois1,rois2,varargin)
purpose: Stack two sets of ROIs. This will stack either the instances or the timecourses themselves dependent on the type.

usage: tf = checkMatchingVar(v,varname,scanNums,<'groupName=?'>,<'taskNum=?'>,<phaseNum=?'>,<'segmentNum=?'>)
purpose: Check that the variable varname had the same values in all scans listed in scanNums. This can be used if, for example, you have run your stimulus program with the same random seed and you want to make sure that the event times were the same.

Can do

• z-scoring
• pca

Need to fill this function section up ..

can do classification with

• Fisher
• svm

get solution via optimization toolbox (quadratic programming solver) Need to fill this function section up ..