EEG-Based Detection of Brisk Walking Motor Imagery Using Feature Transformation Techniques

Abstract

Recently motor imagery (MI) based Brain-Computer Interface (BCI) for lower limb rehabilitation is gaining attention. Feature extraction and dimensionality reduction are crucial signal processing blocks that determine the performance of a BCI system. In this work, various features, that are, band power (BP) features, autoregressive (AAR) parameters and Hjorth (HJ) parameters, widely used in BCI research are studied for their efficacy in discriminating MI brisk walking activity from the idle state. Feature transformation (FT) techniques, a type of dimensionality reduction techniques, namely Principal Component Analysis (PCA), Locality Preserving Projections (LPP) and Local Fisher Discriminant analysis (LFDA) are then applied on the extracted features to map them into a lower dimensional subspace. Ten-fold cross-validation is used to choose the dimension of the projection subspace. In a group of five novice users, it is observed that none of these features separately or all taken together represented the activity well. On using FT techniques, the discriminability of the fused features improved. Among the three techniques, LFDA performed the best showing an average increase in classification accuracy (26.9%), sensitivity (37.6%) and specificity (26.2%) over the average values obtained when no FT technique are used for the group of five subjects.