A discriminative spectral-temporal feature set for motor imagery classification

Abstract

This paper presents a novel technique for motor imagery event classification. Extraction of discriminative feature is a key to accurate classification. To realize this objective we have explored the use of nonnegative matrix factorization (NNMF) for sparse representation of our input signal and determining the discriminative basis vector. We extract both spectral as well as temporal features from this representation to construct our features set. Band power has been shown to be a powerful discriminative feature of the spectral domain for motor imagery classes. Time Domain Parameter (TDP) taken as a temporal feature measures power of EEG using first few derivatives. Our approach is novel in proposing a fusion of both these features. We have used Hierarchical Alternating Least Square (HALS) as a convergence solution to minimize error function of NNMF as it converges more rapidly as compared to other methods. The proposed feature set has been tested using LDA and SVM classifiers technique for classification of 4-class motor imagery signals. We have compared our approach with others presented in literature using the Dataset 2a of BCI competition IV and has shown that our approach achieves the highest reported mean kappa value of 0.62 with the SVM classifier.