Edge AI-Brain-Computer Interfaces System: A Survey.

Nguyen, M-D; Do, T; Tran, X-T; Nguyen, Q-T; Lin, C-T

Edge AI-Brain-Computer Interfaces System: A Survey.

Nguyen, M-D Do, T Tran, X-T Nguyen, Q-T 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, 2025, 33, pp. 4051-4066
Issue Date:: 2025

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Field	Value	Language
dc.contributor.author	Nguyen, M-D
dc.contributor.author	Do, T
dc.contributor.author	Tran, X-T
dc.contributor.author	Nguyen, Q-T
dc.contributor.author	Lin, C-T
dc.date.accessioned	2026-01-30T02:21:55Z
dc.date.available	2026-01-30T02:21:55Z
dc.date.issued	2025
dc.identifier.citation	IEEE Trans Neural Syst Rehabil Eng, 2025, 33, pp. 4051-4066
dc.identifier.issn	1534-4320
dc.identifier.issn	1558-0210
dc.identifier.uri	http://hdl.handle.net/10453/192602
dc.description.abstract	Edge artificial intelligence (Edge AI) has emerged as a transformative paradigm for enhancing the performance, portability, and autonomy of brain-computer interface (BCI) systems. By integrating advanced AI capabilities directly into electroencephalography (EEG)-based devices, Edge AI enables real-time signal processing, reduces dependence on external computational resources, and improves data privacy. However, deploying AI on resource-constrained hardware introduces challenges related to computational capacity, power consumption, and system latency. This survey provides a comprehensive examination of Edge AI-enabled BCI systems, covering the full pipeline from EEG hardware specifications and on-device data acquisition to signal preprocessing techniques and lightweight deep learning models optimized for embedded platforms. We review existing frameworks, specialized hardware accelerators, and energy-efficient AI approaches that facilitate real-time BCI processing at the edge. Furthermore, the paper reviews state-of-the-art solutions, examines key technical challenges, and outlines future research directions in hardware-software co-design and application development. This work aims to serve as a reference for researchers and practitioners seeking to design efficient, portable, and practical Edge AI-powered BCI systems.
dc.format	Print
dc.language	eng
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation	http://purl.org/au-research/grants/arc/DP220100803
dc.relation	http://purl.org/au-research/grants/nhmrc/APP2021183
dc.relation	http://purl.org/au-research/grants/arc/IH240100016
dc.relation	http://purl.org/au-research/grants/arc/DP250103612
dc.relation.ispartof	IEEE Trans Neural Syst Rehabil Eng
dc.relation.isbasedon	10.1109/TNSRE.2025.3618688
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0903 Biomedical Engineering, 0906 Electrical and Electronic Engineering
dc.subject.classification	Biomedical Engineering
dc.subject.classification	4003 Biomedical engineering
dc.subject.classification	4007 Control engineering, mechatronics and robotics
dc.subject.mesh	Brain-Computer Interfaces
dc.subject.mesh	Humans
dc.subject.mesh	Electroencephalography
dc.subject.mesh	Artificial Intelligence
dc.subject.mesh	Signal Processing, Computer-Assisted
dc.subject.mesh	Deep Learning
dc.subject.mesh	Algorithms
dc.subject.mesh	Surveys and Questionnaires
dc.subject.mesh	Software
dc.subject.mesh	Equipment Design
dc.subject.mesh	Humans
dc.subject.mesh	Electroencephalography
dc.subject.mesh	Equipment Design
dc.subject.mesh	Algorithms
dc.subject.mesh	Artificial Intelligence
dc.subject.mesh	Signal Processing, Computer-Assisted
dc.subject.mesh	Software
dc.subject.mesh	Brain-Computer Interfaces
dc.subject.mesh	Surveys and Questionnaires
dc.subject.mesh	Deep Learning
dc.subject.mesh	Brain-Computer Interfaces
dc.subject.mesh	Humans
dc.subject.mesh	Electroencephalography
dc.subject.mesh	Artificial Intelligence
dc.subject.mesh	Signal Processing, Computer-Assisted
dc.subject.mesh	Deep Learning
dc.subject.mesh	Algorithms
dc.subject.mesh	Surveys and Questionnaires
dc.subject.mesh	Software
dc.subject.mesh	Equipment Design
dc.title	Edge AI-Brain-Computer Interfaces System: A Survey.
dc.type	Journal Article
utslib.citation.volume	33
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/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Australian Artificial Intelligence Institute (AAII)
pubs.organisational-group	University of Technology Sydney/UTS Groups/Stroke Research Collaborative
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/Engineering and IT Related HDR Students
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2026-01-30T02:21:54Z
pubs.publication-status	Published
pubs.volume	33

Abstract:

Edge artificial intelligence (Edge AI) has emerged as a transformative paradigm for enhancing the performance, portability, and autonomy of brain-computer interface (BCI) systems. By integrating advanced AI capabilities directly into electroencephalography (EEG)-based devices, Edge AI enables real-time signal processing, reduces dependence on external computational resources, and improves data privacy. However, deploying AI on resource-constrained hardware introduces challenges related to computational capacity, power consumption, and system latency. This survey provides a comprehensive examination of Edge AI-enabled BCI systems, covering the full pipeline from EEG hardware specifications and on-device data acquisition to signal preprocessing techniques and lightweight deep learning models optimized for embedded platforms. We review existing frameworks, specialized hardware accelerators, and energy-efficient AI approaches that facilitate real-time BCI processing at the edge. Furthermore, the paper reviews state-of-the-art solutions, examines key technical challenges, and outlines future research directions in hardware-software co-design and application development. This work aims to serve as a reference for researchers and practitioners seeking to design efficient, portable, and practical Edge AI-powered BCI systems.

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http://hdl.handle.net/10453/192602