EEG data classification with SWLDA using matlab - matlab

Question

I want to ask your help in EEG data classification.
I am a graduate student trying to analyze EEG data.
Now I am struggling with classifying ERP speller (P300) with SWLDA using Matlab
Maybe there is something wrong in my code.
I have read several articles, but they did not cover much details.
My data size is described as below.
size(target) = [300 1856]
size(nontarget) = [998 1856]
row indicates the number of trials, column indicates spanned feature
(I stretched data [64 29] (for visual representation I did not select ROI)
I used stepwisefit function in Matlab to classify target vs non-target
Code is attached below.
ingredients = [targets; nontargets];
heat = [class_targets; class_nontargets]; % target: 1, non-target: -1
randomized_set = shuffle([ingredients heat]);
for k=1:10 % 10-fold cross validation
parition_factor = ceil(size(randomized_set,1) / 10);
cv_test_idx = (k-1)*parition_factor + 1:min(k * parition_factor, size(randomized_set,1));
total_idx = 1:size(randomized_set,1);
cv_train_idx = total_idx(~ismember(total_idx, cv_test_idx));
ingredients = randomized_set(cv_train_idx, 1:end-1);
heat = randomized_set(cv_train_idx, end);
[W,SE,PVAL,INMODEL,STATS,NEXTSTEP,HISTORY]= stepwisefit(ingredients, heat, 'penter', .1);
valid_id = find(INMODEL==1);
v_weights = W(valid_id)';
t_ingredients = randomized_set(cv_test_idx, 1:end-1);
t_heat = randomized_set(cv_test_idx, end); % true labels for test set
v_features = t_ingredients(:, valid_id);
v_weights = repmat(v_weights, size(v_features, 1), 1);
predictor = sum(v_weights .* v_features, 2);
m_result = predictor > 0; % class A: +1, B: 0
t_heat(t_heat==-1) = 0;
acc(k) = sum(m_result==t_heat) / length(m_result);
end
p.s. my code is currently very inefficient and might be bad..
In my assumption, stepwisefit calculates significant coefficients every steps, and valid column would be remained.
Even though it's not LDA, but for binary classification, LDA and linear regression are not different.
However, results were almost random chance.. (for other binary data on the internet, it worked..)
I think I made something wrong, and your help can correct me.
I will appreciate any suggestion and tips to implement classifier for ERP speller.
Or any idea for implementing SWLDA in Matlab code?

Answer 1

The name SWLDA is only used in the context of Brain Computer Interfaces, but I bet it has another name in a more general context.
If you track the recipe of SWLDA you will end up in Krusienski 2006 papers ("A comparison..." and "Toward enhanced P300..") and from there the book where stepwise logarithmic regression is explained: "Draper Smith, Applied Regression Analysis, 1981". However, as far as I am aware of, no paper gives actually the complete recipe on how to implement it (and their details and secrets).
My approach was using stepwiseglm:
H=predictors;
TH=variables;
lbs=labels % (1,2)
if (stepwiseflag)
mdl = stepwiseglm(H', lbs'-1,'constant','upper','linear','distr','binomial');
if (mdl.NumEstimatedCoefficients>1)
inmodel = [];
for i=2:mdl.NumEstimatedCoefficients
inmodel = [inmodel str2num(mdl.CoefficientNames{i}(2:end))];
end
H = H(inmodel,:);
TH = TH(inmodel,:);
end
end
lbls = classify(TH',H',lbs','linear');
You can also use a k-fold cross validaton approach using matlab cvpartition.
c = cvpartition(lbs,'k',10);
opts = statset('display','iter');
fun = #(XT,yT,Xt,yt)...
(sum(~strcmp(yt,classify(Xt,XT,yT,'linear'))));