Classification of mental tasks from EEG data using backtracking search optimization based neural classifier

Abstract

Brain Computer Interface (BCI) has been applied to augment impaired human cognitive function by converting mental signals into control signals. This paper presents a neural classifier optimized using Backtracking Search optimization Algorithm (BSANN) to classify three mental tasks consisting of right or left hand movement imagination and generation of word. BSA is an Evolutionary Algorithm (EA) which is suitable for deciphering non-linear and non-differentiable problems. Single control parameter gives BSA an upshot over other EA due to the lower degree of randomness. BSA keeps memory of old population to generate a new candidate set i.e. solution, so it gets the advantage of utilizing the search results of the previous population. The proposed method (BSANN) has been tested on the publicly available datasets of BCI Competition 3-5. Experimental result shows that BSANN exhibits better results than 21 other algorithms for classification of mental tasks in terms of classification accuracy.