Early Detection of Human Decision-Making in Concealed Object Visual Searching Tasks: An EEG-BiLSTM Study

Tran, X-T; Do, TT-T; Lin, C-T

Early Detection of Human Decision-Making in Concealed Object Visual Searching Tasks: An EEG-BiLSTM Study

Tran, X-T Do, TT-T Lin, C-T

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2023, 2023, pp. 1-4
Issue Date:: 2023-12-11

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Field	Value	Language
dc.contributor.author	Tran, X-T
dc.contributor.author	Do, TT-T
dc.contributor.author	Lin, C-T
dc.date	2023-07-24
dc.date.accessioned	2024-03-05T01:26:56Z
dc.date.available	2024-03-05T01:26:56Z
dc.date.issued	2023-12-11
dc.identifier.citation	2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2023, 2023, pp. 1-4
dc.identifier.isbn	9798350324471
dc.identifier.issn	2375-7477
dc.identifier.issn	2694-0604
dc.identifier.uri	http://hdl.handle.net/10453/176111
dc.description.abstract	Detecting concealed objects presents a significant challenge for human and artificial intelligent systems Detecting concealed objects task necessitates a high level of human attention and cognitive effort to complete the task successfully Thus in this study we use concealed objects as stimuli for our decision making experimental paradigms to quantify participants decision making performance We applied a deep learning model Bi directional Long Short Term Memory BiLSTM to predict the participant s decision accuracy by using their electroencephalogram EEG signals as input The classifier model demonstrated high accuracy reaching 96 1 with an epoching time range of 500 ms following the stimulus event onset The results revealed that the parietal occipital brain region provides highly informative information for the classifier in the concealed visual searching tasks Furthermore the neural mechanism underlying the concealed visual searching and decision making process was explained by analyzing serial EEG components The findings of this study could contribute to the development of a fault alert system which has the potential to improve human decision making performance
dc.format	Print
dc.language	en
dc.publisher	IEEE
dc.relation	http://purl.org/au-research/grants/arc/DP210101093
dc.relation	United States Department of the NavyN629091912058
dc.relation	http://purl.org/au-research/grants/arc/DP220100803
dc.relation.ispartof	2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
dc.relation.ispartof	2023 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
dc.relation.ispartofseries	IEEE Engineering in Medicine and Biology Society Conference Proceedings
dc.relation.isbasedon	10.1109/embc40787.2023.10340547
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.mesh	Humans
dc.subject.mesh	Electroencephalography
dc.subject.mesh	Brain
dc.subject.mesh	Artificial Intelligence
dc.subject.mesh	Brain Mapping
dc.subject.mesh	Attention
dc.subject.mesh	Brain
dc.subject.mesh	Humans
dc.subject.mesh	Electroencephalography
dc.subject.mesh	Brain Mapping
dc.subject.mesh	Attention
dc.subject.mesh	Artificial Intelligence
dc.subject.mesh	Humans
dc.subject.mesh	Electroencephalography
dc.subject.mesh	Brain
dc.subject.mesh	Artificial Intelligence
dc.subject.mesh	Brain Mapping
dc.subject.mesh	Attention
dc.title	Early Detection of Human Decision-Making in Concealed Object Visual Searching Tasks: An EEG-BiLSTM Study
dc.type	Conference Proceeding
utslib.citation.volume	2023
utslib.location.activity	Sydney, Australia
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	*
dc.date.updated	2024-03-05T01:26:53Z
pubs.finish-date	2023-07-27
pubs.publication-status	Published
pubs.start-date	2023-07-24
pubs.volume	2023

Abstract:

Detecting concealed objects presents a significant challenge for human and artificial intelligent systems Detecting concealed objects task necessitates a high level of human attention and cognitive effort to complete the task successfully Thus in this study we use concealed objects as stimuli for our decision making experimental paradigms to quantify participants decision making performance We applied a deep learning model Bi directional Long Short Term Memory BiLSTM to predict the participant s decision accuracy by using their electroencephalogram EEG signals as input The classifier model demonstrated high accuracy reaching 96 1 with an epoching time range of 500 ms following the stimulus event onset The results revealed that the parietal occipital brain region provides highly informative information for the classifier in the concealed visual searching tasks Furthermore the neural mechanism underlying the concealed visual searching and decision making process was explained by analyzing serial EEG components The findings of this study could contribute to the development of a fault alert system which has the potential to improve human decision making performance

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

http://hdl.handle.net/10453/176111