Investigating Feature Ranking Methods for Sub-Band and Relative Power Features in Motor Imagery Task Classification

Algorithm 1

Algorithm for the proposed approach.

Input: EEG Signal for each trail, Class Label No. of Subject = n. No. of trails = T. No. of Segment = m
Output: Accuracy, precision, recall, F-Score
for subject i = 1 : n
for segment j = 1 : T
for trail k = 1 : T
Data = Import data from each trail,
Pro_data = Preprocessing using Butterworth filter of band [0.5–100 HZ] for Data (k)
Features = Wavelet (Processed data with dB 4,5 level)
calculate avg power, Variance of power, Relative power to alpha;
Feature_set = Feature, Class (k);
end
Updated_Feature = Update feature set after each trail by concatenation;
end
Updated_Feature_Final = Update feature set after each segment by concatenation;
end
Confusion Matrix = Function_Classifier (Updated_Feature_Final, Cross validation (k times));
Accuracy, Precision, Recall, F-Score = [Obtain from confusion matrix];
	Calculate Ranks of features from Mutual Information, chi-square, Correlation and Repeat the steps to calculate the matrix and Accuracy