hmm
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@@ -1,107 +1,178 @@
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close all;clc;
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%%
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% Inputs:
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% --------
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% MAVClass1: the features of the VF case (stimulus and rest features)
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% MAVClass2: the features of the Pinch case (stimulus and rest features)
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% TriggerClass1: labels for VF features (stimulus or rest label)
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% TriggerClass2: labels for Pinch features (stimulus or rest label)
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%% c3_classification_complete.m
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% This script performs 10-fold cross-validation for Part 2.3 of the assignment.
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% It answers:
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% 1. Classifiability of Stimuli vs Rest
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% 2. Classifiability of Stimuli vs Stimuli
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% 3. Comparison of MAV vs VAR features
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% 4. Evaluation of Confusion Matrices
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% Build the datasets
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MAV_class1 = MAVClass1(find(TriggerClass1==1));
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MAV_rest1 = MAVClass1(find(TriggerClass1==0));
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clearvars -except filteredFlex filteredPinch filteredVF flexLabels pinchLabels VFLabels fs;
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clc;
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VAR_class1 = VARClass1(find(TriggerClass1==1));
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VAR_rest1 = VARClass1(find(TriggerClass1==0));
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% Check if data is loaded
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if ~exist('filteredFlex', 'var')
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error('Error: Filtered signals not found. Please run c1_dataVis.m first.');
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end
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MAV_class2 = MAVClass2(find(TriggerClass2==1));
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MAV_rest2 = MAVClass2(find(TriggerClass2==0));
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%% 1. Feature Extraction (WSize=100ms, Olap=0)
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fprintf('1. Extracting Features (100ms Window, 0%% Overlap)...\n');
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VAR_class2 = VARClass2(find(TriggerClass2==1));
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VAR_rest2 = VARClass2(find(TriggerClass2==0));
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WSize_sec = 0.1;
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Olap_pct = 0;
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WSize = floor(WSize_sec * fs);
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nOlap = floor(Olap_pct * WSize);
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hop = WSize - nOlap;
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% Concantenate the rest classes
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MAV_rest = [MAV_rest1 MAV_rest2];
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VAR_rest = [VAR_rest1 VAR_rest2];
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% Organize data for looping
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% Index 1=VF, 2=Flex, 3=Pinch
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sigs = {filteredVF, filteredFlex, filteredPinch};
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lbls = {VFLabels, flexLabels, pinchLabels};
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names = {'VF', 'Flex', 'Pinch'};
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feats = struct(); % Structure to hold features
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%%
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% Class1 vs Rest dataset
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MAV_Data_Class1vsRest = [MAV_class1 MAV_rest];
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MAV_Labels_Class1vsRest = [ones(1,length(MAV_class1)) 2*ones(1,length(MAV_rest))];
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for k = 1:3
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sig = sigs{k};
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lab = lbls{k};
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nx = length(sig);
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len = fix((nx - (WSize - hop)) / hop);
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MAV_vec = zeros(1, len);
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VAR_vec = zeros(1, len);
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LBL_vec = zeros(1, len);
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Rise = gettrigger(lab, 0.5);
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Fall = gettrigger(-lab, -0.5);
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for i = 1:len
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idx_start = (i-1)*hop + 1;
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idx_end = idx_start + WSize - 1;
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segment = sig(idx_start:idx_end);
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MAV_vec(i) = mean(abs(segment));
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VAR_vec(i) = var(segment);
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% Label: 1 if window is strictly inside stimulation
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is_stim = any(idx_start >= Rise & idx_end <= Fall);
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LBL_vec(i) = double(is_stim);
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end
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feats(k).MAV = MAV_vec;
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feats(k).VAR = VAR_vec;
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feats(k).LBL = LBL_vec;
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feats(k).Name = names{k};
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end
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VAR_Data_Class1vsRest = [VAR_class1 VAR_rest];
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VAR_Labels_Class1vsRest = MAV_Labels_Class1vsRest;
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%% 2. Define Comparisons
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% We need to run classification for these specific pairs:
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comparisons = {
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'VF vs Rest', 1, 0; % 0 denotes "Rest" class
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'Flex vs Rest', 2, 0;
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'Pinch vs Rest', 3, 0;
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'Flex vs Pinch', 2, 3;
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'Flex vs VF', 2, 1;
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'Pinch vs VF', 3, 1;
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};
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% Class2 vs Rest dataset
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MAV_Data_Class2vsRest = [MAV_class2 MAV_rest];
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MAV_Labels_Class2vsRest = [ones(1,length(MAV_class2)) 2*ones(1,length(MAV_rest))];
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%% 3. Classification Loop (10-Fold CV)
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fprintf('\n2. Running 10-Fold Cross Validation...\n');
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fprintf('----------------------------------------------------------------\n');
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fprintf('%-20s | %-12s | %-12s | %-15s\n', 'Comparison', 'Acc (MAV)', 'Acc (VAR)', 'Best Feature');
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fprintf('----------------------------------------------------------------\n');
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VAR_Data_Class2vsRest = [VAR_class2 VAR_rest];
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VAR_Labels_Class2vsRest = MAV_Labels_Class2vsRest;
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for c = 1:size(comparisons, 1)
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comp_name = comparisons{c, 1};
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idx1 = comparisons{c, 2};
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idx2 = comparisons{c, 3};
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% --- Prepare Data for Class 1 ---
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% Get Stimulus features (Label == 1)
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f1_MAV = feats(idx1).MAV(feats(idx1).LBL == 1);
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f1_VAR = feats(idx1).VAR(feats(idx1).LBL == 1);
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% --- Prepare Data for Class 2 (or Rest) ---
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if idx2 == 0
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% If comparing vs Rest, get Rest features (Label == 0) from the SAME signal
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f2_MAV = feats(idx1).MAV(feats(idx1).LBL == 0);
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f2_VAR = feats(idx1).VAR(feats(idx1).LBL == 0);
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label_names = {feats(idx1).Name, 'Rest'};
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else
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% If comparing vs another Stimulus, get Stimulus features (Label == 1)
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f2_MAV = feats(idx2).MAV(feats(idx2).LBL == 1);
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f2_VAR = feats(idx2).VAR(feats(idx2).LBL == 1);
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label_names = {feats(idx1).Name, feats(idx2).Name};
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end
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% Combine Data
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X_MAV = [f1_MAV, f2_MAV]'; % Transpose to column vector
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X_VAR = [f1_VAR, f2_VAR]';
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% Create Labels (1 for Class 1, 2 for Class 2)
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Y = [ones(length(f1_MAV), 1); 2 * ones(length(f2_MAV), 1)];
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% --- 10-Fold Cross Validation ---
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k = 10;
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cv = cvpartition(Y, 'KFold', k); % Random split (answering Q4!)
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acc_mav = 0;
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acc_var = 0;
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conf_mav = zeros(2,2); % Accumulate confusion matrix
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for i = 1:k
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train_idx = cv.training(i);
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test_idx = cv.test(i);
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% MAV Classification
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pred_mav = classify(X_MAV(test_idx), X_MAV(train_idx), Y(train_idx));
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acc_mav = acc_mav + sum(pred_mav == Y(test_idx)) / length(pred_mav);
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% Build Confusion Matrix for MAV (just one example needed for assignment)
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% Rows = True Class, Cols = Predicted Class
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current_conf = confusionmat(Y(test_idx), pred_mav);
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% Handle edge case if a fold misses a class
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if size(current_conf,1) == 2
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conf_mav = conf_mav + current_conf;
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end
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% Class1 vs Class2 dataset
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MAV_Data_Class1vsClass2 = [MAV_class1 MAV_class2];
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MAV_Labels_Class1vsClass2 = [ones(1,length(MAV_class1)) 2*ones(1,length(MAV_class2))];
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% VAR Classification
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pred_var = classify(X_VAR(test_idx), X_VAR(train_idx), Y(train_idx));
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acc_var = acc_var + sum(pred_var == Y(test_idx)) / length(pred_var);
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end
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% Average Accuracy
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mean_acc_mav = (acc_mav / k) * 100;
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mean_acc_var = (acc_var / k) * 100;
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% Determine Winner
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if mean_acc_mav > mean_acc_var
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winner = 'MAV';
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elseif mean_acc_var > mean_acc_mav
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winner = 'VAR';
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else
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winner = 'Tie';
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end
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fprintf('%-20s | %-11.1f%% | %-11.1f%% | %-15s\n', comp_name, mean_acc_mav, mean_acc_var, winner);
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% --- Display Confusion Matrix for MAV ---
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% Only printing logic to keep output clean, answering "Observe confusion matrices"
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fprintf(' Confusion Matrix (MAV) for %s:\n', comp_name);
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fprintf(' True %-6s: [ %4d %4d ] (Predicted %s / %s)\n', label_names{1}, conf_mav(1,1), conf_mav(1,2), label_names{1}, label_names{2});
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fprintf(' True %-6s: [ %4d %4d ]\n\n', label_names{2}, conf_mav(2,1), conf_mav(2,2));
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end
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fprintf('----------------------------------------------------------------\n');
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VAR_Data_Class1vsClass2 = [VAR_class1 VAR_class2];
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VAR_Labels_Class1vsClass2 = MAV_Labels_Class1vsClass2;
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%%
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% Both feature datasets
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MAVVAR_Data_Class1vsRest = [MAV_Data_Class1vsRest; VAR_Data_Class1vsRest];
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MAVVAR_Labels_Class1vsRest = MAV_Labels_Class1vsRest;
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MAVVAR_Data_Class2vsRest = [MAV_Data_Class2vsRest; VAR_Data_Class2vsRest];
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MAVVAR_Labels_Class2vsRest = MAV_Labels_Class2vsRest;
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MAVVAR_Data_Class1vsClass2 = [MAV_Data_Class1vsClass2; VAR_Data_Class1vsClass2];
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MAVVAR_Labels_Class1vsClass2 = MAV_Labels_Class1vsClass2;
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%%
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% Classify all combinations (training set)
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k = 10; % for k-fold cross validation
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c1 = cvpartition(length(MAV_Labels_Class1vsRest),'KFold',k);
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c2 = cvpartition(length(VAR_Labels_Class1vsRest),'KFold',k);
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c3 = cvpartition(length(MAVVAR_Labels_Class1vsRest),'KFold',k);
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c4 = cvpartition(length(MAV_Labels_Class2vsRest),'KFold',k);
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c5 = cvpartition(length(VAR_Labels_Class2vsRest),'KFold',k);
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c6 = cvpartition(length(MAVVAR_Labels_Class2vsRest),'KFold',k);
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c7 = cvpartition(length(MAV_Labels_Class1vsClass2),'KFold',k);
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c8 = cvpartition(length(VAR_Labels_Class1vsClass2),'KFold',k);
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c9 = cvpartition(length(MAVVAR_Labels_Class1vsClass2),'KFold',k);
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% Repeat the following for i=1:k, and average performance metrics across all iterations
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i=1;
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% loop over all k-folds and avergae the performance
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% for i=1:k
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[TstMAVFC1Rest TstMAVErrC1Rest] = classify(MAV_Data_Class1vsRest(c1.test(i))',MAV_Data_Class1vsRest(c1.training(i))',MAV_Labels_Class1vsRest(c1.training(i)));
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[TstCM_MAV_C1rest dum1 TstAcc_MAV_C1rest dum2] = confusion(MAV_Labels_Class1vsRest(c1.test(i)), TstMAVFC1Rest);
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[TstVARFC1Rest TstVARErrC1Rest] = classify(VAR_Data_Class1vsRest(c2.test(i))',VAR_Data_Class1vsRest(c2.training(i))',VAR_Labels_Class1vsRest(c2.training(i)));
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[TstCM_VAR_C1rest dum1 TstAcc_VAR_C1rest dum2] = confusion(VAR_Labels_Class1vsRest(c2.test(i)), TstVARFC1Rest);
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[TstMAVVARFC1Rest TstMAVVARErrC1Rest] = classify(MAVVAR_Data_Class1vsRest(:,c3.test(i))',MAVVAR_Data_Class1vsRest(:,c3.training(i))',MAVVAR_Labels_Class1vsRest(c3.training(i)));
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[TstCM_MAVVAR_C1rest dum1 TstAcc_MAVVAR_C1rest dum2] = confusion(MAVVAR_Labels_Class1vsRest(c3.test(i)), TstMAVVARFC1Rest);
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% Class2 vs Rest
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[TstMAVFC2Rest TstMAVErrC2Rest] = classify(MAV_Data_Class2vsRest(c4.test(i))',MAV_Data_Class2vsRest(c4.training(i))',MAV_Labels_Class2vsRest(c4.training(i)));
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[TstCM_MAV_C2rest dum1 TstAcc_MAV_C2rest dum2] = confusion(MAV_Labels_Class2vsRest(c4.test(i)), TstMAVFC2Rest);
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[TstVARFC2Rest TstVARErrC2Rest] = classify(VAR_Data_Class2vsRest(c5.test(i))',VAR_Data_Class2vsRest(c5.training(i))',VAR_Labels_Class2vsRest(c5.training(i)));
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[TstCM_VAR_C2rest dum1 TstAcc_VAR_C2rest dum2] = confusion(VAR_Labels_Class2vsRest(c5.test(i)), TstVARFC2Rest);
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[TstMAVVARFC2Rest TstMAVVARErrC2Rest] = classify(MAVVAR_Data_Class2vsRest(:,c6.test(i))',MAVVAR_Data_Class2vsRest(:,c6.training(i))',MAVVAR_Labels_Class2vsRest(c6.training(i)));
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[TstCM_MAVVAR_C2rest dum1 TstAcc_MAVVAR_C2rest dum2] = confusion(MAVVAR_Labels_Class2vsRest(c6.test(i)), TstMAVVARFC2Rest);
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% Class1 vs Class2
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[TstMAVFC1C2 TstMAVErrC1C2] = classify(MAV_Data_Class1vsClass2(c7.test(i))',MAV_Data_Class1vsClass2(c7.training(i))',MAV_Labels_Class1vsClass2(c7.training(i)));
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[TstCM_MAV_C1C2 dum1 TstAcc_MAV_C1C2 dum2] = confusion(MAV_Labels_Class1vsClass2(c7.test(i)), TstMAVFC1C2);
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[TstVARFC1C2 TstVARErrC1C2] = classify(VAR_Data_Class1vsClass2(c8.test(i))',VAR_Data_Class1vsClass2(c8.training(i))',VAR_Labels_Class1vsClass2(c8.training(i)));
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[TstCM_VAR_C1C2 dum1 TstAcc_VAR_C1C2 dum2] = confusion(VAR_Labels_Class1vsClass2(c8.test(i)), TstVARFC1C2);
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[TstMAVVARFC1C2 TstMAVVARErrC1C2] = classify(MAVVAR_Data_Class1vsClass2(:,c9.test(i))',MAVVAR_Data_Class1vsClass2(:,c9.training(i))',MAVVAR_Labels_Class1vsClass2(c9.training(i)));
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[TstCM_MAVVAR_C1C2 dum1 TstAcc_MAVVAR_C1C2 dum2] = confusion(MAVVAR_Labels_Class1vsClass2(c9.test(i)), TstMAVVARFC1C2);
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% end
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%%
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%% 4. Answer Prompts
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fprintf('\n=== Automated Analysis for Part 2.3 ===\n');
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fprintf('1. Check "Pinch vs Rest" accuracy above. Is it low? (Likely yes, due to low SNR).\n');
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fprintf('2. Check "Flex vs Pinch". Can they be distinguished?\n');
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fprintf('3. Observe the Confusion Matrices: Are they balanced? \n');
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fprintf(' - If one class is predicted much more often, the classifier is biased.\n');
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fprintf('4. Feature Performance: Look at the "Best Feature" column.\n');
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fprintf(' - MAV is typically more robust for these signals.\n');
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fprintf('5. Validation Fairness (Assignment Q4):\n');
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fprintf(' - This script uses "cvpartition", which splits data RANDOMLY.\n');
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fprintf(' - Since EMG/ENG signals are time-series, random splitting causes "data leakage"\n');
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fprintf(' (training on samples immediately adjacent to test samples).\n');
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fprintf(' - Therefore, this is likely NOT a fair assessment of generalization.\n');
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