Jaya Based ANFIS for Monitoring of Two Class Motor Imagery Task

The brain-computer interface (BCI) identifies brain patterns to translate thoughts into action. The identification relies on the performance of the classifier. In this paper, identification and monitoring of electroencephalogram-based BCI for motor imagery (MI) task is proposed by an efficient adaptive neuro-fuzzy classifier (NFC). The Jaya optimization algorithm is integrated with adaptive neuro-fuzzy inference systems to enhance classification accuracy. The linguistic hedge (LH) is used for proper elicitation and pruning of the fuzzy rules and network is trained using scaled conjugate gradient (SCG) and speeding up SCG (SSCG) techniques. In this paper, Jaya-based k-means is applied to divide the feature set into two mutually exclusive clusters and fire the fuzzy rule. The performance of the proposed classifier, Jaya-based NFC using SSCG as training algorithm and is powered by LH (JayaNFCSSCGLH), is compared with four different NFCs for classifying two class MI-based tasks. We observed a shortening of computation time per iteration by 57.78% in the case of SSCG as compared with the SCG technique of training. LH-based feature selecting capability of the proposed classifier not only reduces computation time but also improves the accuracy by discarding irrelevant features. Lesser computation time with fast convergence and high accuracy among considered NFCs make it a suitable choice for the real-time application. Supremacy of JayaNFCSSCGLH among the considered classifier is validated through Friedman test. Classification result is used to control switching of light emitting diode, turning thoughts into action.