Generating a fuzzy rule-based brain-state-drift detector by riemann-metric-based clustering

Chang, YC; Wang, YK; Wu, D; Lin, CT

Generating a fuzzy rule-based brain-state-drift detector by riemann-metric-based clustering

Chang, YC Wang, YK

Wu, D Lin, CT

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Publication Type:: Conference Proceeding
Citation:: 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017, 2017, 2017-January pp. 1220 - 1225
Issue Date:: 2017-11-27

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Field	Value	Language
dc.contributor.author	Chang, YC	en_US
dc.contributor.author	Wang, YK https://orcid.org/0000-0001-8390-2664	en_US
dc.contributor.author	Wu, D	en_US
dc.contributor.author	Lin, CT https://orcid.org/0000-0001-8371-8197	en_US
dc.date.available	2020-05-25T19:10:27Z
dc.date.issued	2017-11-27	en_US
dc.identifier.citation	2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017, 2017, 2017-January pp. 1220 - 1225	en_US
dc.identifier.isbn	9781538616451	en_US
dc.identifier.uri	http://hdl.handle.net/10453/125519
dc.description.abstract	© 2017 IEEE. Brain-state drifts could significantly impact on the performance of machine-learning algorithms in brain computer interface (BCI). However, less is understood with regard to how brain transition states influence a model and how it can be represented for a system. Herein we are interested in the hidden information of brain state-drift occurring in both simulated and real-world human-system interaction. This research introduced the Riemann metric to categorize EEG data, and visualized the clustering result so that the distribution of the data can be observable. Moreover, to defeat subjective uncertainty of electroencephalography (EEG) signals, fuzzy theory was employed. In this study, we built a fuzzy rule-based brain-statedrift detector to observe the brain state and imported data from different subjects to testify the performance. The result of the detection is acceptable and shown in this paper. In the future, we expect that brain-state drifting can be connected with human behaviors via the proposed fuzzy rule-based classification. We also will develop a new structure for a fuzzy rule-based brain-statedrift detector to improve the detection accuracy.	en_US
dc.relation.ispartof	2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017	en_US
dc.relation.isbasedon	10.1109/SMC.2017.8122779	en_US
dc.title	Generating a fuzzy rule-based brain-state-drift detector by riemann-metric-based clustering	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2017-January	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	open_access
pubs.publication-status	Published	en_US
pubs.volume	2017-January	en_US

Abstract:

© 2017 IEEE. Brain-state drifts could significantly impact on the performance of machine-learning algorithms in brain computer interface (BCI). However, less is understood with regard to how brain transition states influence a model and how it can be represented for a system. Herein we are interested in the hidden information of brain state-drift occurring in both simulated and real-world human-system interaction. This research introduced the Riemann metric to categorize EEG data, and visualized the clustering result so that the distribution of the data can be observable. Moreover, to defeat subjective uncertainty of electroencephalography (EEG) signals, fuzzy theory was employed. In this study, we built a fuzzy rule-based brain-statedrift detector to observe the brain state and imported data from different subjects to testify the performance. The result of the detection is acceptable and shown in this paper. In the future, we expect that brain-state drifting can be connected with human behaviors via the proposed fuzzy rule-based classification. We also will develop a new structure for a fuzzy rule-based brain-statedrift detector to improve the detection accuracy.

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http://hdl.handle.net/10453/125519