EEG-based covert speech decoding using random rotation extreme learning machine ensemble for intuitive BCI communication

Abstract

Background and motivation:Brain–computer interfaces (BCI) assist communication for the handicapped and disabled. Previous work has shown that it is usually non-intuitive, and it frequently employs Electroencephalogram (EEG) based motor imagery. Ensemble learning has been shown to be reliable in a variety of BCI classification tasks, including motor imagery and P300 event-related potential. The purpose of ensemble learning, according to past research, is to get trained accurate but diverse base classifiers to increase generalization performance. Extreme Learning Machine (ELM) with fast learning speed has been proven effective in classification applications and suitable for ensemble learning due to its randomness. The suitability of ensemble methods for intuitive BCI communication using EEG data of different covert speech words is investigated in this work. Methods:To achieve this goal, we construct a random rotation based kernel ELM ensemble, resulting in very diverse classifiers which can learn through transformed spaces. Minimize error ensemble pruning is used to prune low complementarity classifiers, and consequently, combining the remaining high complementarity classifiers to get the final ensemble classifier. Eight subjects performed covert speech tasks which involved mentally repeating five words namely; ‘left’, ‘right’, ‘up’, ‘down’, and ‘stop’. Results:We achieved classification accuracy with pruning and without pruning of 63.67% and 57.13% respectively. Experimental results show that, in comparison to other classification techniques, the proposed technique produces competitive results. Conclusion and Significance:Our findings indicate the potential of random rotation kernel ELM ensemble pruning to effectively classify EEG-based covert speech signals. This proposed research involving an ensemble method for the classification of covert speech words can make a significant contribution towards intuitive BCI development using silent speech.