Spatial Filtering for EEG-Based Regression Problems in Brain-Computer Interface (BCI)

Wu, D; King, JT; Chuang, CH; Lin, CT; Jung, TP

Spatial Filtering for EEG-Based Regression Problems in Brain-Computer Interface (BCI)

Wu, D King, JT Chuang, CH

Lin, CT

Jung, TP

Permalink

Publication Type:: Journal Article
Citation:: IEEE Transactions on Fuzzy Systems, 2018, 26 (2), pp. 771 - 781
Issue Date:: 2018-04-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download Accepted ManuscriptAdobe PDF (563.55 kB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Wu, D	en_US
dc.contributor.author	King, JT	en_US
dc.contributor.author	Chuang, CH https://orcid.org/0000-0002-5043-8380	en_US
dc.contributor.author	Lin, CT https://orcid.org/0000-0001-8371-8197	en_US
dc.contributor.author	Jung, TP	en_US
dc.date.available	2020-05-25T19:04:24Z
dc.date.issued	2018-04-01	en_US
dc.identifier.citation	IEEE Transactions on Fuzzy Systems, 2018, 26 (2), pp. 771 - 781	en_US
dc.identifier.issn	1063-6706	en_US
dc.identifier.uri	http://hdl.handle.net/10453/124758
dc.description.abstract	© 1993-2012 IEEE. Electroencephalogram (EEG) signals are frequently used in brain-computer interfaces (BCIs), but they are easily contaminated by artifacts and noise, so preprocessing must be done before they are fed into a machine learning algorithm for classification or regression. Spatial filters have been widely used to increase the signal-to-noise ratio of EEG for BCI classification problems, but their applications in BCI regression problems have been very limited. This paper proposes two common spatial pattern (CSP) filters for EEG-based regression problems in BCI, which are extended from the CSP filter for classification, by using fuzzy sets. Experimental results on EEG-based response speed estimation from a large-scale study, which collected 143 sessions of sustained-attention psychomotor vigilance task data from 17 subjects during a 5-month period, demonstrate that the two proposed spatial filters can significantly increase the EEG signal quality. When used in LASSO and k-nearest neighbors regression for user response speed estimation, the spatial filters can reduce the root-mean-square estimation error by 10.02-19.77\%, and at the same time increase the correlation to the true response speed by 19.39-86.47\%.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP150101645
dc.relation.ispartof	IEEE Transactions on Fuzzy Systems	en_US
dc.relation.isbasedon	10.1109/TFUZZ.2017.2688423	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Spatial Filtering for EEG-Based Regression Problems in Brain-Computer Interface (BCI)	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	26	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	0102 Applied Mathematics	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.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	26	en_US

Abstract:

© 1993-2012 IEEE. Electroencephalogram (EEG) signals are frequently used in brain-computer interfaces (BCIs), but they are easily contaminated by artifacts and noise, so preprocessing must be done before they are fed into a machine learning algorithm for classification or regression. Spatial filters have been widely used to increase the signal-to-noise ratio of EEG for BCI classification problems, but their applications in BCI regression problems have been very limited. This paper proposes two common spatial pattern (CSP) filters for EEG-based regression problems in BCI, which are extended from the CSP filter for classification, by using fuzzy sets. Experimental results on EEG-based response speed estimation from a large-scale study, which collected 143 sessions of sustained-attention psychomotor vigilance task data from 17 subjects during a 5-month period, demonstrate that the two proposed spatial filters can significantly increase the EEG signal quality. When used in LASSO and k-nearest neighbors regression for user response speed estimation, the spatial filters can reduce the root-mean-square estimation error by 10.02-19.77\%, and at the same time increase the correlation to the true response speed by 19.39-86.47\%.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/124758