Brain EEG signal processing for controlling a robotic arm

Abstract

Researchers recently proposed new scientific methods for restoring function to those with motor impairments. one of these methods is to provide the brain with a new non-muscular communication and control channel, a direct Brain-Machine Interface (BMI). This paper presents a Brain Machine Interface (BMI) system based on using the brain electroencephalography (EEG) signals associated with 3 arm movements (close, open arm and close hand) for controlling a robotic arm. Signals recorded from one subject using Emotive Epoc device. Four channels only were used, in our experiment, AF3, which located at the prefrontal cortex and F7, F3, FC5 which located at the supplementary motor cortex of the brain. Three different techniques were used for features extraction which are: Wavelet Transform (WT), Fast Fourier Transform (FFT) and Principal Component Analysis (PCA). Multi-layer Perceptron Neural Network trained by a standard back propagation algorithm was used for classifying the three considered tasks. Classification rates of 91.1%, 86.7% and 85.6% were achieved with the three used features extraction techniques respectively. Experimental results show that the proposed system achieved high classification rates than other systems in the same application.