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Prosthetic motor imaginary task classification based on EEG quality assessment features

Haggag, Sherif, Mohamed, Shady, Haggag, Omar and Nahavandi, Saeid 2015, Prosthetic motor imaginary task classification based on EEG quality assessment features, in ICONIP 2015 : Neural information processing : 22nd International Conference, ICONIP 2015, Istanbul, Turkey, November 9-12, 2015 : proceedings, Springer, New York, N.Y., pp. 87-94, doi: 10.1007/978-3-319-26561-2_11.

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Title Prosthetic motor imaginary task classification based on EEG quality assessment features
Author(s) Haggag, Sherif
Mohamed, ShadyORCID iD for Mohamed, Shady orcid.org/0000-0002-8851-1635
Haggag, Omar
Nahavandi, Saeid
Conference name Neural Information Processing. Conference (22nd : 2015 : Istanbul, Turkey)
Conference location Istanbul, Turkey
Conference dates 9-12 Nov. 2015
Title of proceedings ICONIP 2015 : Neural information processing : 22nd International Conference, ICONIP 2015, Istanbul, Turkey, November 9-12, 2015 : proceedings
Publication date 2015
Series Lecture Notes in Computer Science v.9492
Start page 87
End page 94
Total pages 8
Publisher Springer
Place of publication New York, N.Y.
Summary Brain Computer Interface (BCI) plays an important role in the communication between human and machines. This communication is based on the human brain signals. In these systems, users use their brain instead of the limbs or body movements to do tasks. The brain signals are analyzed and translated into commands to control any communication devices, robots or computers. In this paper, the aim was to enhance the performance of a brain computer interface (BCI) systems through better prosthetic motor imaginary tasks classification. The challenging part is to use only a single channel of electroencephalography (EEG). Arm movement imagination is the task of the user, where (s)he was asked to imagine moving his arm up or down. Our system detected the imagination based on the input brain signal. Some EEG quality features were extracted from the brain signal, and the Decision Tree was used to classify the participant's imagination based on the extracted features. Our system is online which means that it can give the decision as soon as the signal is given to the system (takes only 20 ms). Also, only one EEG channel is used for classification which reduces the complexity of the system which leads to fast performance. Hundred signals were used for testing, on average 97.4% of the up-down prosthetic motor imaginary tasks were detected correctly. This method can be used in many different applications such as: moving artificial limbs and wheelchairs due to it's high speed and accuracy.
ISBN 9783319265605
ISSN 0302-9743
1611-3349
Language eng
DOI 10.1007/978-3-319-26561-2_11
Field of Research 08 Information And Computing Sciences
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, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30082436

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