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Ass. Lect. Mohamed Fawzy Ibrahim Issa :: Publications:

Title:
Brain EEG Signal Processing For Controlling a Robotic Arm
Authors: Howida A.Shedeed,Mohamed F.Issa,Salah M.El-sayed
Year: 2013
Keywords: Not Available
Journal: Not Available
Volume: Not Available
Issue: Not Available
Pages: Not Available
Publisher: Not Available
Local/International: International
Paper Link:
Full paper Mohamed Fawzy Ibrahim Issa_paper1.pdf
Supplementary materials Not Available
Abstract:

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.

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