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Prosthetic motor imaginary task classification using single channel of electroencephalography

conference contribution
posted on 2015-01-01, 00:00 authored by Sherif Haggag, Shady MohamedShady Mohamed, Hussein Haggag, Saeid NahavandiSaeid Nahavandi
Brain Computer Interface (BCI) is playing a very important role in human machine communications. Recent communication systems depend on the brain signals for communication. In these systems, users clearly manipulate their brain activity rather than using motor movements in order to generate signals that could be used to give commands and control any communication devices, robots or computers. In this paper, the aim was to estimate the performance of a brain computer interface (BCI) system by detecting the prosthetic motor imaginary tasks by using only a single channel of electroencephalography (EEG). The participant is asked to imagine moving his arm up or down and our system detects the movement based on the participant brain signal. Some features are extracted from the brain signal using Mel-Frequency Cepstrum Coefficient and based on these feature a Hidden Markov model is used to help in knowing if the participant imagined moving up or down. The major advantage in our method is that only one channel is needed to take the decision. Moreover, the method is online which means that it can give the decision as soon as the signal is given to the system. Hundred signals were used for testing, on average 89 % of the up down prosthetic motor imaginary tasks were detected correctly. This method can be used in many different applications such as: moving artificial prosthetic limbs and wheelchairs due to it's high speed and accuracy.



IEEE International Conference on Systems, Man, and Cybernetics (2015 : Hong Kong, China)


969 - 973




Hong Kong, China

Place of publication

Piscataway, N.J.

Start date


End date








Publication classification

E Conference publication; E1 Full written paper - refereed

Copyright notice

2015, IEEE

Title of proceedings

SMC 2015 : Big Data Analytics for Human-Centric Systems. Proceedings of the 2015 IEEE International Conference on Systems, Man, and Cybernetics