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.