A combined EEG and EOG signals based wheelchair control in virtual environment

In this paper, a combining Electroencephalographic (EEG) and Electro-oculographic (EOG) approach has been developed in order to allow the control of the displacement of a wheelchair by disable peoples. NeuroSky MindeWave headset and wet superficial electrodes have been used for the measurement of EEG and EOG signals respectively. These signals have been processed using Wavelet Transform (WT) and Principal Components Analysis (PCA) method. The obtained results have been compared for feature extraction in term of classification accuracy. The classification has been performed using artificial neural network (ANN) where a classification accuracy rate of 93% has been reached. The output of the classifier has been used successfully to control the displacement of a wheelchair in a virtual environment.