Classification of Multiclass Motor Imagery EEG Signal using Sparsity Approach

Sreeja S R, Debasis Samanta

Abstract:

Motor imagery (MI) based brain–computer interface systems involving multiple tasks are highly required
in many real-time applications such as hands and touch-free text entry, prosthetic arms, virtual real-
ity systems, movement of a wheel chair, cursor movement, etc. The classification of MI data is the core
computing in all these systems. However, the existing classification techniques are either computation-
ally expensive or not so accurate or both. To address this limitation, in this work, a sparse representation
based classification technique has been proposed to classify multi-tasks MI electroencephalogram data.
The proposed method computes only wavelet energy directly from the segmented MI data and constructs
a dictionary. The sparse representation from the dictionary is then used to classify given a test data. The
proposed approach is faster as it works with only a single feature and without the need for any pre-
processing. Further, with a reduced length of an imaging period, the proposed method provides accurate
classification in a lesser computation time. The performance of the proposed approach has been evalu-
ated and also compared with other classifiers reported in the literature. The results substantiate that the
proposed sparsity approach performs significantly better than the existing classifiers.