Enhance Reading Comprehension from EEG-Based Brain-Computer Interface

Liu, X; Cao, Z

Enhance Reading Comprehension from EEG-Based Brain-Computer Interface

Liu, X Cao, Z

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Publisher:: SPRINGER-VERLAG SINGAPORE PTE LTD
Publication Type:: Chapter
Citation:: AI 2023: Advances in Artificial Intelligence, 2024, 14471 LNAI, pp. 545-555
Issue Date:: 2024-01-01

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Field	Value	Language
dc.contributor.author	Liu, X
dc.contributor.author	Cao, Z https://orcid.org/0000-0003-3656-0328
dc.contributor.editor	Liu, T
dc.contributor.editor	Yue, L
dc.contributor.editor	Webb, G
dc.contributor.editor	Wang, D
dc.date.accessioned	2024-09-06T03:53:32Z
dc.date.available	2024-09-06T03:53:32Z
dc.date.issued	2024-01-01
dc.identifier.citation	AI 2023: Advances in Artificial Intelligence, 2024, 14471 LNAI, pp. 545-555
dc.identifier.isbn	978-981-99-8387-2
dc.identifier.uri	http://hdl.handle.net/10453/180728
dc.description.abstract	Electroencephalography (EEG)-based brain-computer interfaces (BCIs) have emerged as a valuable technology for decoding human cognitive processes, including reading attention and cognitive loads. While previous studies have explored eye fixations during word recognition, the intricacies of brain dynamics involved in sentence comprehension in the temporal or spectral domains still need to be discovered. Addressing this gap is crucial for enhancing learning processes; thus, in this study, we propose the first acquisition and recognition of event-related potentials and spectral perturbations using channel and independent component analysis, based on sentence-level simultaneous EEG and eye-tracking recorded from human natural reading tasks. Our results showed peaks of brain activation evoked at around 162 ms (approaching 200 ms) after the stimulus (starting to read each sentence) in the occipital area, indicating the onset timing of human retrieving lexical and semantic visual information processing. Approximately 200 ms occipital area presented increased alpha power and decreased beta and gamma power, relative to the baseline. Our results implied that most semantic-perception responses occurred around 200 ms in alpha, beta and gamma bands to facilitate human reading representation. The implications of our study underscore the significance of EEG-based BCI applications in reading tasks, serving as a potential catalyst for improving cognitive attention and comprehension in end-user reading and learning experiences. By retrieving the intricate cognitive mechanisms underlying sentence comprehension, we pave the way for developing brain-computer learning strategies to optimise reading instruction and support a diverse range of users.
dc.language	en
dc.publisher	SPRINGER-VERLAG SINGAPORE PTE LTD
dc.relation.ispartof	AI 2023: Advances in Artificial Intelligence
dc.relation.ispartofseries	Lecture Notes in Artificial Intelligence
dc.relation.isbasedon	10.1007/978-981-99-8388-9_44
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.classification	46 Information and computing sciences
dc.title	Enhance Reading Comprehension from EEG-Based Brain-Computer Interface
dc.type	Chapter
utslib.citation.volume	14471 LNAI
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
utslib.copyright.status	closed_access	*
dc.date.updated	2024-09-06T03:53:30Z
pubs.publication-status	Published
pubs.volume	14471 LNAI

Abstract:

Electroencephalography (EEG)-based brain-computer interfaces (BCIs) have emerged as a valuable technology for decoding human cognitive processes, including reading attention and cognitive loads. While previous studies have explored eye fixations during word recognition, the intricacies of brain dynamics involved in sentence comprehension in the temporal or spectral domains still need to be discovered. Addressing this gap is crucial for enhancing learning processes; thus, in this study, we propose the first acquisition and recognition of event-related potentials and spectral perturbations using channel and independent component analysis, based on sentence-level simultaneous EEG and eye-tracking recorded from human natural reading tasks. Our results showed peaks of brain activation evoked at around 162 ms (approaching 200 ms) after the stimulus (starting to read each sentence) in the occipital area, indicating the onset timing of human retrieving lexical and semantic visual information processing. Approximately 200 ms occipital area presented increased alpha power and decreased beta and gamma power, relative to the baseline. Our results implied that most semantic-perception responses occurred around 200 ms in alpha, beta and gamma bands to facilitate human reading representation. The implications of our study underscore the significance of EEG-based BCI applications in reading tasks, serving as a potential catalyst for improving cognitive attention and comprehension in end-user reading and learning experiences. By retrieving the intricate cognitive mechanisms underlying sentence comprehension, we pave the way for developing brain-computer learning strategies to optimise reading instruction and support a diverse range of users.

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http://hdl.handle.net/10453/180728