Fuzzy divergence based analysis for eeg drowsiness detection brain computer interfaces

Reddy, TK; Arora, V; Behera, L; Wang, YK; Lin, CT

Fuzzy divergence based analysis for eeg drowsiness detection brain computer interfaces

Reddy, TK Arora, V Behera, L Wang, YK Lin, CT

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: IEEE International Conference on Fuzzy Systems, 2020, 2020-July, pp. 1-7
Issue Date:: 2020-07-01

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Field	Value	Language
dc.contributor.author	Reddy, TK
dc.contributor.author	Arora, V
dc.contributor.author	Behera, L
dc.contributor.author	Wang, YK
dc.contributor.author	Lin, CT
dc.date	2020-07-19
dc.date.accessioned	2021-03-16T00:53:58Z
dc.date.available	2021-03-16T00:53:58Z
dc.date.issued	2020-07-01
dc.identifier.citation	IEEE International Conference on Fuzzy Systems, 2020, 2020-July, pp. 1-7
dc.identifier.isbn	9781728169323
dc.identifier.issn	1098-7584
dc.identifier.uri	http://hdl.handle.net/10453/147215
dc.description.abstract	© 2020 IEEE. EEG signals can be processed and classified into commands for brain-computer interface (BCI). Stable deciphering of EEG is one of the leading challenges in BCI design owing to low signal to noise ratio and non-stationarities. Presence of non-stationarities in the EEG signals significantly perturb the feature distribution thus deteriorating the performance of Brain Computer Interface. Stationary Subspace methods discover subspaces in which data distribution remains steady over time. In this paper, we develop novel spatial filtering based feature extraction methods for dealing with nonstationarity in EEG signals from a drowsiness detection problem (a machine learning regression problem). The proposed method: DivOVR-FuzzyCSP-WS based features clearly outperformed fuzzy CSP based baseline features in terms of both RMSE and CC performance metrics. It is hoped that the proposed feature extraction method based on DivOVR-FuzzyCSP-WS will bring in a lot of interest in researchers working in developing algorithms for signal processing, in general, for BCI regression problems.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	IEEE International Conference on Fuzzy Systems
dc.relation.ispartof	2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
dc.relation.isbasedon	10.1109/FUZZ48607.2020.9177833
dc.rights	© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.title	Fuzzy divergence based analysis for eeg drowsiness detection brain computer interfaces
dc.type	Conference Proceeding
utslib.citation.volume	2020-July
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/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2022-08-26T00:00:00+1000Z
dc.date.updated	2021-03-16T00:53:56Z
pubs.finish-date	2020-07-24
pubs.publication-status	Published
pubs.start-date	2020-07-19
pubs.volume	2020-July

Abstract:

© 2020 IEEE. EEG signals can be processed and classified into commands for brain-computer interface (BCI). Stable deciphering of EEG is one of the leading challenges in BCI design owing to low signal to noise ratio and non-stationarities. Presence of non-stationarities in the EEG signals significantly perturb the feature distribution thus deteriorating the performance of Brain Computer Interface. Stationary Subspace methods discover subspaces in which data distribution remains steady over time. In this paper, we develop novel spatial filtering based feature extraction methods for dealing with nonstationarity in EEG signals from a drowsiness detection problem (a machine learning regression problem). The proposed method: DivOVR-FuzzyCSP-WS based features clearly outperformed fuzzy CSP based baseline features in terms of both RMSE and CC performance metrics. It is hoped that the proposed feature extraction method based on DivOVR-FuzzyCSP-WS will bring in a lot of interest in researchers working in developing algorithms for signal processing, in general, for BCI regression problems.

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http://hdl.handle.net/10453/147215