Influence of stimuli colour in SSVEP-based BCI wheelchair control using support vector machines

• Published in: Journal of medical engineering & technology Vol. 38; no. 3; pp. 125 - 134
• Main Authors: Singla, Rajesh, Khosla, Arun, Jha, Rameshwar
• Format: Journal Article
• Language: English
• Published: England Informa UK Ltd 01.04.2014; Taylor & Francis
• Subjects: Adult; ANN; brain computer interface; Brain-Computer Interfaces; Color; Electroencephalography - methods; Evoked Potentials, Visual - physiology; Female; Humans; Male; Neural Networks (Computer); Regression Analysis; Signal Processing, Computer-Assisted; steady state visual evoked potential; Support Vector Machine; support vector machines; Wheelchairs; Young Adult
• ISSN: 0309-1902; 1464-522X; 1464-522X
• DOI: 10.3109/03091902.2014.884179

Abstract This study aims to develop a Steady State Visual Evoked Potential (SSVEP)-based Brain Computer Interface (BCI) system to control a wheelchair, with improving accuracy as the major goal. The developed wheelchair can move in forward, backward, left, right and stop positions. Four different flickering frequencies in the low frequency region were used to elicit the SSVEPs and were displayed on a Liquid Crystal Display (LCD) monitor using LabVIEW. Four colours (green, red, blue and violet) were included in the study to investigate the colour influence in SSVEPs. The Electroencephalogram (EEG) signals recorded from the occipital region were first segmented into 1 s windows and features were extracted by using Fast Fourier Transform (FFT). Three different classifiers, two based on Artificial Neural Network (ANN) and one based on Support Vector Machine (SVM), were compared to yield better accuracy. Twenty subjects participated in the experiment and the accuracy was calculated by considering the number of correct detections produced while performing a pre-defined movement sequence. SSVEP with violet colour showed higher performance than green and red. The One-Against-All (OAA) based multi-class SVM classifier showed better accuracy than the ANN classifiers. The classification accuracy over 20 subjects varies between 75-100%, while information transfer rates (ITR) varies from 12.13-27 bpm for BCI wheelchair control with SSVEPs elicited by violet colour stimuli and classified using OAA-SVM.