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Prosthetic motor imaginary task classification using single channel of electroencephalography

Haggag, Sherif, Mohamed, Shady, Haggag, Hussein and Nahavandi, Saeid 2015, Prosthetic motor imaginary task classification using single channel of electroencephalography, in SMC 2015 : Big Data Analytics for Human-Centric Systems. Proceedings of the 2015 IEEE International Conference on Systems, Man, and Cybernetics, IEEE, Piscataway, N.J., pp. 969-973, doi: 10.1109/SMC.2015.176.

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Title Prosthetic motor imaginary task classification using single channel of electroencephalography
Author(s) Haggag, Sherif
Mohamed, ShadyORCID iD for Mohamed, Shady orcid.org/0000-0002-8851-1635
Haggag, Hussein
Nahavandi, SaeidORCID iD for Nahavandi, Saeid orcid.org/0000-0002-0360-5270
Conference name IEEE International Conference on Systems, Man, and Cybernetics (2015 : Hong Kong, China)
Conference location Hong Kong, China
Conference dates 9-12 Oct. 2015
Title of proceedings SMC 2015 : Big Data Analytics for Human-Centric Systems. Proceedings of the 2015 IEEE International Conference on Systems, Man, and Cybernetics
Publication date 2015
Start page 969
End page 973
Total pages 5
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) Science & Technology
Technology
Computer Science, Cybernetics
Computer Science, Information Systems
Computer Science, Theory & Methods
Computer Science
EEG
Prosthetic Motor Imaginary Task
Neural Signal
BCI
HMM
MFCC
BRAIN-COMPUTER INTERFACES
EEG SIGNALS
FEATURES
PEOPLE
SYSTEM
Summary 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.
ISBN 9781479986965
ISSN 1062-922X
Language eng
DOI 10.1109/SMC.2015.176
Field of Research 110999 Neurosciences not elsewhere classified
Socio Economic Objective 970101 Expanding Knowledge in the Mathematical Sciences
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2015, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30082433

Document type: Conference Paper
Collection: Centre for Intelligent Systems Research
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