36 lines
1.3 KiB
Mathematica
36 lines
1.3 KiB
Mathematica
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filteredSignal = ; % bandapass filtered signal
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label = ; % labels of stimulus locations
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WSize = ; % window size in s
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Olap = ; % overlap percentage
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%% Extracting Features over overlapping windows
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WSize = floor(WSize*fs); % length of each data frame, 30ms
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nOlap = floor(Olap*WSize); % overlap of successive frames, half of WSize
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hop = WSize-nOlap; % amount to advance for next data frame
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nx = length(signal); % length of input vector
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len = fix((nx - (WSize-hop))/hop); %length of output vector = total frames
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% preallocate outputs for speed
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[MAV_feature, VAR_feature, featureLabels] = deal(zeros(1,len));
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Rise1 = gettrigger(label,0.5); % gets the starting points of stimulations
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Fall1 = gettrigger(-label,-0.5); % gets the ending points of stimulations
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for i = 1:len
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segment = filteredSignal(((i-1)*hop+1):((i-1)*hop+WSize));
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MAV_feature(i) = ;
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VAR_feature(i) = ;
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% re-build the label vector to match it with the feature vector
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featureLabels(i) = sum(arrayfun(@(t) ((i-1)*hop+1) >= Rise1(t) && ((i-1)*hop+WSize) <= Fall1(t), 1:length(Rise1)));
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end
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%% Plotting the features
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% Note: when plotting the features, scale the featureLabels to the max of
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% the feature values for proper visualization
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