Belief f-divergence for EEG complexity evaluation

Huang, J; Song, X; Xiao, F; Cao, Z; Lin, CT

Belief f-divergence for EEG complexity evaluation

Huang, J Song, X Xiao, F Cao, Z

Lin, CT

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Publisher:: ELSEVIER SCIENCE INC
Publication Type:: Journal Article
Citation:: Information Sciences, 2023, 643
Issue Date:: 2023-09-01

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Field	Value	Language
dc.contributor.author	Huang, J
dc.contributor.author	Song, X
dc.contributor.author	Xiao, F
dc.contributor.author	Cao, Z https://orcid.org/0000-0003-3656-0328
dc.contributor.author	Lin, CT
dc.date.accessioned	2024-03-05T01:07:30Z
dc.date.available	2024-03-05T01:07:30Z
dc.date.issued	2023-09-01
dc.identifier.citation	Information Sciences, 2023, 643
dc.identifier.issn	0020-0255
dc.identifier.issn	1872-6291
dc.identifier.uri	http://hdl.handle.net/10453/176104
dc.description.abstract	Evidence theory, as a useful uncertain reasoning method, is widely used in various fields. Nevertheless, how to quantify the divergence between the basic belief assignments in belief theory and how to apply it are still open issues. To measure the discrepancy between basic belief assignments, we first introduce belief f-divergence (BFD) in this paper. We also analyze and verify its related properties that are expected to significantly improve the performance of electroencephalogram (EEG) complexity evaluation. Then, we employ empirical mode decomposition to detrend EEG noise and reconstruct EEG signals with inherent components and develop a belief f-divergence-based complexity (BFDC) evaluation method with five different functions, including belief KL divergence, belief Hellinger divergence, belief χ2 divergence, belief triangular divergence, and belief total variation divergence, to evaluate the complexity of the EEG signals. Based on a benchmark lane-keeping EEG driving experiment, our proposed BFDC evaluation approach can identify the differences among keep–drift events more obviously and significantly reduce the root mean squared error up to the order of 102 compared to the existing complexity measurement of fuzzy entropy. We believe that our proposed BFDC evaluation method has the potential to be a new and robust approach to precisely evaluate the dynamic complexity of physiological signals.
dc.language	English
dc.publisher	ELSEVIER SCIENCE INC
dc.relation.ispartof	Information Sciences
dc.relation.isbasedon	10.1016/j.ins.2023.119189
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	01 Mathematical Sciences, 08 Information and Computing Sciences, 09 Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.classification	40 Engineering
dc.subject.classification	46 Information and computing sciences
dc.subject.classification	49 Mathematical sciences
dc.title	Belief f-divergence for EEG complexity evaluation
dc.type	Journal Article
utslib.citation.volume	643
utslib.for	01 Mathematical Sciences
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
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	embargoed	*
utslib.copyright.embargo	2025-09-01T00:00:00+1000Z
dc.date.updated	2024-03-05T01:07:27Z
pubs.publication-status	Published
pubs.volume	643

Abstract:

Evidence theory, as a useful uncertain reasoning method, is widely used in various fields. Nevertheless, how to quantify the divergence between the basic belief assignments in belief theory and how to apply it are still open issues. To measure the discrepancy between basic belief assignments, we first introduce belief f-divergence (BFD) in this paper. We also analyze and verify its related properties that are expected to significantly improve the performance of electroencephalogram (EEG) complexity evaluation. Then, we employ empirical mode decomposition to detrend EEG noise and reconstruct EEG signals with inherent components and develop a belief f-divergence-based complexity (BFDC) evaluation method with five different functions, including belief KL divergence, belief Hellinger divergence, belief χ2 divergence, belief triangular divergence, and belief total variation divergence, to evaluate the complexity of the EEG signals. Based on a benchmark lane-keeping EEG driving experiment, our proposed BFDC evaluation approach can identify the differences among keep–drift events more obviously and significantly reduce the root mean squared error up to the order of 102 compared to the existing complexity measurement of fuzzy entropy. We believe that our proposed BFDC evaluation method has the potential to be a new and robust approach to precisely evaluate the dynamic complexity of physiological signals.

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http://hdl.handle.net/10453/176104