Classification of vision perception using EEG signals for brain computer interface

Abstract

Patients suffering from Motor Neuron Disease (MND) and semi-paralysis have trouble to maneuver a conventional wheelchair independently. As a response, this research was conducted whereby an individual’s visual perception can associate to movement controls. The designed system could later on be integrated into an autonomous wheelchair. The Brain Computer Interface (BCI) system would require the Electroencephalography (EEG) signal to be recorded from the subject using Mindset24 EEG amplifier. Subsequently, the signals’ noise content was been analysed with analysis of variance (ANOVA) whereby signal with high noise content was removed from the samples. Then, spectral energy of different bands of EEG signal (θ, α, β1, β2, β3 and γ) pertaining to an individual’s visual perception were extracted. Next, dimension reduction was performed to select band features based on feature separability using Devijver’s Feature Index (DFI) and Principle Component Analysis (PCA). Finally, neural network models, namely, multi-layered perceptron (MLP), Elman Recurrent Neural Network (ERNN) and nonlinear exogenous autoregressive model (NARX) have been designed to as classifiers to determine the subject’s visual perception, with an average accuracy of over 90%. Among the trained classifier, ERNN was chosen for it yielded a relatively higher performance in the both the Locational Matching and Image Recognition Paradigm in terms of classification accuracies (97.75% and 97.81% respectively). Therefore ERNN is the most suitable classifier to be used for application of visual perception to help MND patient navigate in a wheelchair.