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

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.

Support vector machines to detect physiological patterns for EEG and EMG-based human–computer interaction: a review

“…This result was by 0.9% higher than the one obtained with an optimal electrode recombination method for feature extraction. For SSVEPs classification, instead, in [68] authors developed a prototype of BCI-controlled wheelchair. SSVEPs elicited by four different flickering frequencies were used to control the movement of the wheelchair in four directions.…”

“…Being easily recognizable in the EEG spectrum, SSVEPs have been widely used to implement BCIs, with high accuracy and throughput rates, devoted e.g. to the selection of buttons on a screen [66], to the communication by means of a speller [67], to the driving of a wheelchair [68], to the control of a robotic arm [69], etc. Finally, there is a class of BCI systems making use of Slow-Cortical Potentials (SCPs), which are slow (<1Hz) negative or positive potential shifts voluntary modulated by a subject to implement a binary communication [70].…”

Preliminary Comparative Experiments of Support Vector Machine and Neural Network for EEG-based BCI Mobile Robot Control

“…Electric wheelchair control and robotic arm control are typical application examples of EEG-based BCI. Singla et al [1,2] developed a Steady State Visual Evoked Potential (SSVEP)-based BCI for controlling a wheelchair using multi-class SVM. Meng et al [3] experimentally investigated a noninvasive BCI for reach and grasp task of robotic arm.…”

Improving EEG-based BCI Neural Networks for Mobile Robot Control by Bayesian Optimization

“…EEG-based BCI have already applied for assisting handicapped people and augmenting human capability 1,2 . R. Single et al developed a SSVEP-based BCI for controlling a wheelchair using multi-class SVM 3,4 . J. Meng et al experimentally investigated a noninvasive BCI for reach and grasp task of robotic arm 5 .…”