Real-time microcontroller based brain computer interface for mental task classifications using wireless eeg signals from two channels

Chai, R; Hunter, GP; Ling, SH; Nguyen, HT

Real-time microcontroller based brain computer interface for mental task classifications using wireless eeg signals from two channels

Chai, R

Hunter, GP Ling, SH

Nguyen, HT

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the 9th IASTED International Conference on Biomedical Engineering, BioMed 2012, 2012, pp. 336 - 342
Issue Date:: 2012-07-16

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Field	Value	Language
dc.contributor.author	Chai, R https://orcid.org/0000-0002-1922-7024	en_US
dc.contributor.author	Hunter, GP	en_US
dc.contributor.author	Ling, SH https://orcid.org/0000-0003-0849-5098	en_US
dc.contributor.author	Nguyen, HT https://orcid.org/0000-0003-3373-8178	en_US
dc.date.issued	2012-07-16	en_US
dc.identifier.citation	Proceedings of the 9th IASTED International Conference on Biomedical Engineering, BioMed 2012, 2012, pp. 336 - 342	en_US
dc.identifier.isbn	9780889869097	en_US
dc.identifier.uri	http://hdl.handle.net/10453/22391
dc.description.abstract	A brain computer interface (BCI) using electroencephalography (EEG) to measure brain activities could provide severely disabled people with alternative means of control and communication. In a practical system, portability, low power and real-time operation are the keys requirements. This could be accomplished by using an embedded microcontroller based system. The main contribution of this paper shows the development of a real-time BCI prototype system to classify groups of mental tasks based on such a system. The relevant mental tasks used are mental arithmetic, figure rotation, letter composing, visual counting and eyes closed action. Moreover, the system uses a separate two channels only wireless EEG measurement module with the active positions at parietal and occipital lobes. The result shows the wireless EEG module has a good performance with a CMRR of more than 95dB. In addition, the size of the module is small (36×36 mm 2) and current consumption is low enough to operate off a 3V coin cell battery. The mental tasks were classified using a feed-forward back-propagation artificial neural network (ANN) trained with the Levenberg-Marquardt algorithm. An accuracy of around 70% was achieved with bit rate at around 0.4 bits/trial for six subjects tested to select between three separate mental tasks.	en_US
dc.relation.ispartof	Proceedings of the 9th IASTED International Conference on Biomedical Engineering, BioMed 2012	en_US
dc.relation.isbasedon	10.2316/P.2012.764-058	en_US
dc.title	Real-time microcontroller based brain computer interface for mental task classifications using wireless eeg signals from two channels	en_US
dc.type	Conference Proceeding
utslib.for	090604 Microelectronics and Integrated Circuits	en_US
utslib.for	090303 Biomedical Instrumentation	en_US
utslib.for	080108 Neural, Evolutionary and Fuzzy Computation	en_US
dc.location.activity	Innsbruck, Austria	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

A brain computer interface (BCI) using electroencephalography (EEG) to measure brain activities could provide severely disabled people with alternative means of control and communication. In a practical system, portability, low power and real-time operation are the keys requirements. This could be accomplished by using an embedded microcontroller based system. The main contribution of this paper shows the development of a real-time BCI prototype system to classify groups of mental tasks based on such a system. The relevant mental tasks used are mental arithmetic, figure rotation, letter composing, visual counting and eyes closed action. Moreover, the system uses a separate two channels only wireless EEG measurement module with the active positions at parietal and occipital lobes. The result shows the wireless EEG module has a good performance with a CMRR of more than 95dB. In addition, the size of the module is small (36×36 mm 2) and current consumption is low enough to operate off a 3V coin cell battery. The mental tasks were classified using a feed-forward back-propagation artificial neural network (ANN) trained with the Levenberg-Marquardt algorithm. An accuracy of around 70% was achieved with bit rate at around 0.4 bits/trial for six subjects tested to select between three separate mental tasks.

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http://hdl.handle.net/10453/22391