EEG Peak Detection in Cognitive Conflict Processing Using Summit Navigator and Clustering-Based Ranking.

Dinh, TH; Singh, AK; Linh Trung, N; Nguyen, DN; Lin, C-T

EEG Peak Detection in Cognitive Conflict Processing Using Summit Navigator and Clustering-Based Ranking.

Dinh, TH Singh, AK Linh Trung, N Nguyen, DN Lin, C-T

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Trans Neural Syst Rehabil Eng, 2022, 30, pp. 1548-1556
Issue Date:: 2022

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Field	Value	Language
dc.contributor.author	Dinh, TH
dc.contributor.author	Singh, AK
dc.contributor.author	Linh Trung, N
dc.contributor.author	Nguyen, DN
dc.contributor.author	Lin, C-T
dc.date.accessioned	2023-03-23T01:02:20Z
dc.date.available	2023-03-23T01:02:20Z
dc.date.issued	2022
dc.identifier.citation	IEEE Trans Neural Syst Rehabil Eng, 2022, 30, pp. 1548-1556
dc.identifier.issn	1534-4320
dc.identifier.issn	1558-0210
dc.identifier.uri	http://hdl.handle.net/10453/168128
dc.description.abstract	Correct detection of peaks in electroencephalogram (EEG) signals is of essence due to the significant correlation of those potentials with cognitive performance and disorders. This paper proposes a novel and non-parametric approach to detect prediction error negativity (PEN) in cognitive conflict processing. The PEN candidates are first located from the input signal via an adaptation of a recent effective method for local maxima extraction, processed in a multi-scale manner. The found candidates are then fused and ranked based on their shape and location-based features. False positives caused by candidates' magnitude are eliminated by rotating the sorted candidate list where the one with the second-best ranking score will be identified as PEN. The EEG data collected from a 3D object selection task have been used to verify the efficacy of the proposed approach. Compared with the state-of-the-art peak detection techniques, the proposed method shows an improvement of at least 2.67% in accuracy and 6.27% in sensitivity while requires only about 4 ms to process an epoch. The accuracy and computational efficiency of the proposed technique in the detection of PEN in cognitive conflict processing would lead to promising applications in performance improvement of brain-computer interfaces (BCIs).
dc.format	Print-Electronic
dc.language	eng
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation	http://purl.org/au-research/grants/arc/DP210101093
dc.relation	http://purl.org/au-research/grants/arc/DP220100803
dc.relation.ispartof	IEEE Trans Neural Syst Rehabil Eng
dc.relation.isbasedon	10.1109/TNSRE.2022.3179255
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0903 Biomedical Engineering, 0906 Electrical and Electronic Engineering
dc.subject.classification	Biomedical Engineering
dc.subject.mesh	Algorithms
dc.subject.mesh	Brain
dc.subject.mesh	Brain-Computer Interfaces
dc.subject.mesh	Cluster Analysis
dc.subject.mesh	Cognition
dc.subject.mesh	Electroencephalography
dc.subject.mesh	Humans
dc.subject.mesh	Signal Processing, Computer-Assisted
dc.subject.mesh	Brain
dc.subject.mesh	Humans
dc.subject.mesh	Electroencephalography
dc.subject.mesh	Cluster Analysis
dc.subject.mesh	Cognition
dc.subject.mesh	Algorithms
dc.subject.mesh	Signal Processing, Computer-Assisted
dc.subject.mesh	Brain-Computer Interfaces
dc.subject.mesh	Algorithms
dc.subject.mesh	Brain
dc.subject.mesh	Brain-Computer Interfaces
dc.subject.mesh	Cluster Analysis
dc.subject.mesh	Cognition
dc.subject.mesh	Electroencephalography
dc.subject.mesh	Humans
dc.subject.mesh	Signal Processing, Computer-Assisted
dc.title	EEG Peak Detection in Cognitive Conflict Processing Using Summit Navigator and Clustering-Based Ranking.
dc.type	Journal Article
utslib.citation.volume	30
utslib.location.activity	United States
utslib.for	0903 Biomedical Engineering
utslib.for	0906 Electrical and Electronic 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 - GBDTC - Global Big Data Technologies
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 Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
dc.date.updated	2023-03-23T01:02:18Z
pubs.publication-status	Published
pubs.volume	30

Abstract:

Correct detection of peaks in electroencephalogram (EEG) signals is of essence due to the significant correlation of those potentials with cognitive performance and disorders. This paper proposes a novel and non-parametric approach to detect prediction error negativity (PEN) in cognitive conflict processing. The PEN candidates are first located from the input signal via an adaptation of a recent effective method for local maxima extraction, processed in a multi-scale manner. The found candidates are then fused and ranked based on their shape and location-based features. False positives caused by candidates' magnitude are eliminated by rotating the sorted candidate list where the one with the second-best ranking score will be identified as PEN. The EEG data collected from a 3D object selection task have been used to verify the efficacy of the proposed approach. Compared with the state-of-the-art peak detection techniques, the proposed method shows an improvement of at least 2.67% in accuracy and 6.27% in sensitivity while requires only about 4 ms to process an epoch. The accuracy and computational efficiency of the proposed technique in the detection of PEN in cognitive conflict processing would lead to promising applications in performance improvement of brain-computer interfaces (BCIs).

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http://hdl.handle.net/10453/168128