Brain computer interface-based smart living environmental auto-adjustment control system in UPnP home networking

Lin, CT; Lin, BS; Lin, FC; Chang, CJ

Brain computer interface-based smart living environmental auto-adjustment control system in UPnP home networking

Lin, CT

Lin, BS Lin, FC Chang, CJ

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Publication Type:: Journal Article
Citation:: IEEE Systems Journal, 2014, 8 (2), pp. 363 - 370
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Lin, CT https://orcid.org/0000-0001-8371-8197	en_US
dc.contributor.author	Lin, BS	en_US
dc.contributor.author	Lin, FC	en_US
dc.contributor.author	Chang, CJ	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	IEEE Systems Journal, 2014, 8 (2), pp. 363 - 370	en_US
dc.identifier.issn	1932-8184	en_US
dc.identifier.uri	http://hdl.handle.net/10453/118324
dc.description.abstract	A brain computer interface-based smart living environmental auto-adjustment control system (BSLEACS) is proposed in this paper. Recently, many environmental control systems have been proposed to improve human quality of life. However, little research has focused on environmental control directly using the human physiological state. Based on the advantage of our technique on brain computer interface (BCI), we integrated the BCI technique with universal plug and play (UPnP) home networking for smart house applications. BSLEACS mainly consists of a wireless physiological signal acquisition module, an embedded signal processing module, a simple control protocol/power line communication environmental controller, and a host system. Here, the physiological signal acquisition module and embedded signal processing module were designed for long-term electroencephalogram (EEG) monitoring and backend analysis, respectively. The advantages of low power consumption and small volume of the above modules are suitable for smart house applications in daily life. Moreover, different from other BCI systems, the property of using only a single EEG channel to monitor cognitive state also makes BSLEACS become more practicable. BSLEACS has been verified in a practical demo room, and the environmental adjustment can be automatically controlled by the change of the user's cognitive state. BSLEACS provides a novel system prototype for environmental control, and can be simply extended and integrated with the UPnP home networking for other applications. © 2012 IEEE.	en_US
dc.relation.ispartof	IEEE Systems Journal	en_US
dc.relation.isbasedon	10.1109/JSYST.2012.2192756	en_US
dc.subject.classification	Operations Research	en_US
dc.title	Brain computer interface-based smart living environmental auto-adjustment control system in UPnP home networking	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	8	en_US
utslib.for	0906 Electrical and Electronic Engineering	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 Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	8	en_US

Abstract:

A brain computer interface-based smart living environmental auto-adjustment control system (BSLEACS) is proposed in this paper. Recently, many environmental control systems have been proposed to improve human quality of life. However, little research has focused on environmental control directly using the human physiological state. Based on the advantage of our technique on brain computer interface (BCI), we integrated the BCI technique with universal plug and play (UPnP) home networking for smart house applications. BSLEACS mainly consists of a wireless physiological signal acquisition module, an embedded signal processing module, a simple control protocol/power line communication environmental controller, and a host system. Here, the physiological signal acquisition module and embedded signal processing module were designed for long-term electroencephalogram (EEG) monitoring and backend analysis, respectively. The advantages of low power consumption and small volume of the above modules are suitable for smart house applications in daily life. Moreover, different from other BCI systems, the property of using only a single EEG channel to monitor cognitive state also makes BSLEACS become more practicable. BSLEACS has been verified in a practical demo room, and the environmental adjustment can be automatically controlled by the change of the user's cognitive state. BSLEACS provides a novel system prototype for environmental control, and can be simply extended and integrated with the UPnP home networking for other applications. © 2012 IEEE.

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http://hdl.handle.net/10453/118324