Distance-based weighted sparse representation to classify motor imagery EEG signals for BCI applications

Abstract

Motor imagery (MI) based brain-computer interface systems (BCIs) are highly in need for a large number of real-time applications such as hands and touch-free text entry system, movement of a wheelchair, movement of a cursor, prosthetic arm movement, virtual reality systems, etc. In recent years, sparse representation-based classification (SRC) is a growing technique and has been a successful technique on classifying MI-based Electroencephalography (EEG) signals. To further boost the proficiency of SRC technique, in this paper, a weighted SRC (WSRC) has been proposed for classifying MI signals. In WSRC approach, a weighted dictionary has been constructed according to the dissimilarity information between a test data and training samples. Then for the given test data, the sparse coefficients are computed over the weighted dictionary using l0-minimization problem. The sparse solution obtained using WSRC gives discriminative information and as a consequence, WSRC proves to be superior for MI-based EEG classification. The experimental results substantiate that WSRC is more efficient and accurate than SRC.