Title: BCI: an optimised speller using SSVEP

Authors: Irshad Ahmad Ansari; Rajesh Singla

Addresses: Department of ASE, Indian Institute of Technology Roorkee, Roorkee 247667, India ' Department of Instrumentation and Control, Dr. B. R. Ambedkar National Institute of Technology Jalandhar, Jalandhar 144011, India

Abstract: The proposed work is done in order to develop an optimised Brain-Computer Interface (BCI) system (speller) for people with severe motor impairments using SSVEP (Steady-State Visual Evoked Potentials). To make the system fast yet error-free, the optimisation of speller is divided into three domains: one is the design of smart encoding method for the selection of appeared characters on interface, second one is the optimal frequency choice and the last one is design of optimal feature classification algorithm. Three classification methods: threshold method, Artificial Neural Network (ANN) and Support Vector Machine (SVM) are evaluated. An optimal user window is also carefully selected after many trails in order to maintain a decent communication rate. The optimised BCI system provides an average accuracy of 96% with character per minute (CPM) of 13 ± 2. Speller performs almost similar with new users too because inter-subject variability is tackle by SVM classifier.

Keywords: brain-computer interface; BCI; SSVEP; steady-state visual evoked potentials; support vector Machines; SVM; artificial neural networks; ANNs; brain speller; EEG signals; severe motor impairment; smart encoding; feature classification; thresholding; optimisation.

DOI: 10.1504/IJBET.2016.078988

International Journal of Biomedical Engineering and Technology, 2016 Vol.22 No.1, pp.31 - 46

Received: 15 May 2015
Accepted: 22 Dec 2015

Published online: 05 Sep 2016 *

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