Weighted sparse representation for classification of motor imagery EEG signals

Sreeja S R, Debasis Samanta, Monalisa sarma

Abstract:

Motor imagery (MI) based brain-computer inter-
face systems (BCIs) are highly in demand for many real-time
applications such as hands and touch-free text entry, prosthetic
arms, virtual reality, movement of wheelchairs, etc. Traditional
sparse representation based classification (SRC) is a thriving
technique in recent years and has been a successful approach for
classifying MI EEG signals. To further improve the capability
of SRC, in this paper, a weighted SRC (WSRC) has been
proposed for classifying two-class MI tasks (right-hand, right-
foot). WSRC constructs a weighted dictionary according to the
dissimilarity information between the test data and the 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 better
discriminative information than SRC and as a consequence,
WSRC proves to be superior for MI EEG classification. The
experimental results substantiate that WSRC is more efficient
and accurate than SRC.