hmm

HW_1_PNS_EE379K-385V_Neural_Eng/c2_featureExtraction.asv

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+
+filteredSignal = VF_filt_signalOfInterest; % bandapass filtered signal 
+label = VFLabels; % labels of stimulus locations
+
+WSize = 50; % window size in s
+Olap = 0; % overlap percentage
+
+%% Extracting Features over overlapping windows
+
+WSize = floor(WSize*fs);	    % length of each data frame, 30ms
+nOlap = floor(Olap*WSize);  % overlap of successive frames, half of WSize
+hop = WSize-nOlap;	    % amount to advance for next data frame
+nx = length(signal);	            % length of input vector
+len = fix((nx - (WSize-hop))/hop);	%length of output vector = total frames
+
+% preallocate outputs for speed
+[MAV_feature, VAR_feature, featureLabels] = deal(zeros(1,len));
+
+Rise1 = gettrigger(label,0.5); % gets the starting points of stimulations
+Fall1 = gettrigger(-label,-0.5); % gets the ending points of stimulations
+
+for i = 1:len
+    segment = filteredSignal(((i-1)*hop+1):((i-1)*hop+WSize));
+    MAV_feature(i) = ;
+    VAR_feature(i) = ;
+    
+    % re-build the label vector to match it with the feature vector
+    featureLabels(i) = sum(arrayfun(@(t) ((i-1)*hop+1) >= Rise1(t) && ((i-1)*hop+WSize) <= Fall1(t), 1:length(Rise1)));
+end
+
+%% Plotting the features
+% Note: when plotting the features, scale the featureLabels to the max of
+% the feature values for proper visualization
+
+