Feature extraction with deep belief networks for driver's cognitive states prediction from EEG data

Hajinoroozi, M; Jung, TP; Lin, CT; Huang, Y

Feature extraction with deep belief networks for driver's cognitive states prediction from EEG data

Hajinoroozi, M Jung, TP Lin, CT

Huang, Y

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Publication Type:: Conference Proceeding
Citation:: 2015 IEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2015 - Proceedings, 2015, pp. 812 - 815
Issue Date:: 2015-08-31

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Field	Value	Language
dc.contributor.author	Hajinoroozi, M	en_US
dc.contributor.author	Jung, TP	en_US
dc.contributor.author	Lin, CT https://orcid.org/0000-0001-8371-8197	en_US
dc.contributor.author	Huang, Y	en_US
dc.date.issued	2015-08-31	en_US
dc.identifier.citation	2015 IEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2015 - Proceedings, 2015, pp. 812 - 815	en_US
dc.identifier.isbn	9781479919482	en_US
dc.identifier.uri	http://hdl.handle.net/10453/119598
dc.description.abstract	© 2015 IEEE. This study considers the prediction of driver's cognitive states from electroencephalographic (EEG) data. Extracting EEG features correlated with driver's cognitive states is key for achieving accurate prediction. However, high dimensionality and temporal-And-spatial correlations of EEG data make extraction of effective features difficult. This study explores the approaches based on deep belief networks (DBN) for feature extraction and dimension reduction. Experimental results of this study showed that DBN applied to channel epochs (DBN-C) produces the most discriminant features and the best classification performance is achieved when DBN-C is applied to the time-frequency and independent-component-Analysis transformed EEG data. The results suggested that DBN-C is a promising new method for extracting complex, discriminant features for EEG-based brain computer interfaces.	en_US
dc.relation.ispartof	2015 IEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2015 - Proceedings	en_US
dc.relation.isbasedon	10.1109/ChinaSIP.2015.7230517	en_US
dc.title	Feature extraction with deep belief networks for driver's cognitive states prediction from EEG data	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	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.publication-status	Published	en_US

Abstract:

© 2015 IEEE. This study considers the prediction of driver's cognitive states from electroencephalographic (EEG) data. Extracting EEG features correlated with driver's cognitive states is key for achieving accurate prediction. However, high dimensionality and temporal-And-spatial correlations of EEG data make extraction of effective features difficult. This study explores the approaches based on deep belief networks (DBN) for feature extraction and dimension reduction. Experimental results of this study showed that DBN applied to channel epochs (DBN-C) produces the most discriminant features and the best classification performance is achieved when DBN-C is applied to the time-frequency and independent-component-Analysis transformed EEG data. The results suggested that DBN-C is a promising new method for extracting complex, discriminant features for EEG-based brain computer interfaces.

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http://hdl.handle.net/10453/119598