Design and verification of a wearable wireless 64-channel high-resolution EEG acquisition system with wi-fi transmission.

Lin, C-T; Wang, Y; Chen, S-F; Huang, K-C; Liao, L-D

Design and verification of a wearable wireless 64-channel high-resolution EEG acquisition system with wi-fi transmission.

Lin, C-T Wang, Y Chen, S-F Huang, K-C Liao, L-D

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Publisher:: SPRINGER HEIDELBERG
Publication Type:: Journal Article
Citation:: Med Biol Eng Comput, 2023, 61, (11), pp. 3003-3019
Issue Date:: 2023-11

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Field	Value	Language
dc.contributor.author	Lin, C-T
dc.contributor.author	Wang, Y
dc.contributor.author	Chen, S-F
dc.contributor.author	Huang, K-C
dc.contributor.author	Liao, L-D
dc.date.accessioned	2024-03-05T01:20:58Z
dc.date.available	2023-06-25
dc.date.available	2024-03-05T01:20:58Z
dc.date.issued	2023-11
dc.identifier.citation	Med Biol Eng Comput, 2023, 61, (11), pp. 3003-3019
dc.identifier.issn	0140-0118
dc.identifier.issn	1741-0444
dc.identifier.uri	http://hdl.handle.net/10453/176109
dc.description.abstract	Brain-computer interfaces (BCIs) allow communication between the brain and the external world. This type of technology has been extensively studied. However, BCI instruments with high signal quality are typically heavy and large. Thus, recording electroencephalography (EEG) signals is an inconvenient task. In recent years, system-on-chip (SoC) approaches have been integrated into BCI research, and sensors for wireless portable devices have been developed; however, there is still considerable work to be done. As neuroscience research has advanced, EEG signal analyses have come to require more accurate data. Due to the limited bandwidth of Bluetooth wireless transmission technology, EEG measurement systems with more than 16 channels must be used to reduce the sampling rate and prevent data loss. Therefore, the goal of this study was to develop a multichannel, high-resolution (24-bit), high-sampling-rate EEG BCI device that transmits signals via Wi-Fi. We believe that this system can be used in neuroscience research. The EEG acquisition system proposed in this work is based on a Cortex-M4 microcontroller with a Wi-Fi subsystem, providing a multichannel design and improved signal quality. This system is compatible with wet sensors, Ag/AgCl electrodes, and dry sensors. A LabVIEW-based user interface receives EEG data via Wi-Fi transmission and saves the raw data for offline analysis. In previous cognitive experiments, event tags have been communicated using Recommended Standard 232 (RS-232). The developed system was validated through event-related potential (ERP) and steady-state visually evoked potential (SSVEP) experiments. Our experimental results demonstrate that this system is suitable for recording EEG measurements and has potential in practical applications. The advantages of the developed system include its high sampling rate and high amplification. Additionally, in the future, Internet of Things (IoT) technology can be integrated into the system for remote real-time analysis or edge computing.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	SPRINGER HEIDELBERG
dc.relation	http://purl.org/au-research/grants/arc/DP210101093
dc.relation	http://purl.org/au-research/grants/arc/DP220100803
dc.relation.ispartof	Med Biol Eng Comput
dc.relation.isbasedon	10.1007/s11517-023-02879-y
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.rights	This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://dx.doi.org/10.1007/s11517-023-02879-y
dc.subject	0903 Biomedical Engineering, 0906 Electrical and Electronic Engineering
dc.subject.classification	Biomedical Engineering
dc.subject.classification	4003 Biomedical engineering
dc.subject.classification	4603 Computer vision and multimedia computation
dc.subject.classification	4611 Machine learning
dc.subject.mesh	Electroencephalography
dc.subject.mesh	Evoked Potentials
dc.subject.mesh	Brain-Computer Interfaces
dc.subject.mesh	Cerebral Cortex
dc.subject.mesh	Wearable Electronic Devices
dc.subject.mesh	Evoked Potentials, Visual
dc.subject.mesh	Cerebral Cortex
dc.subject.mesh	Electroencephalography
dc.subject.mesh	Evoked Potentials
dc.subject.mesh	Evoked Potentials, Visual
dc.subject.mesh	Brain-Computer Interfaces
dc.subject.mesh	Wearable Electronic Devices
dc.subject.mesh	Electroencephalography
dc.subject.mesh	Evoked Potentials
dc.subject.mesh	Brain-Computer Interfaces
dc.subject.mesh	Cerebral Cortex
dc.subject.mesh	Wearable Electronic Devices
dc.subject.mesh	Evoked Potentials, Visual
dc.title	Design and verification of a wearable wireless 64-channel high-resolution EEG acquisition system with wi-fi transmission.
dc.type	Journal Article
utslib.citation.volume	61
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 - 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	2024-08-11T00:00:00+1000Z
dc.date.updated	2024-03-05T01:20:54Z
pubs.issue	11
pubs.publication-status	Published
pubs.volume	61
utslib.citation.issue	11

Abstract:

Brain-computer interfaces (BCIs) allow communication between the brain and the external world. This type of technology has been extensively studied. However, BCI instruments with high signal quality are typically heavy and large. Thus, recording electroencephalography (EEG) signals is an inconvenient task. In recent years, system-on-chip (SoC) approaches have been integrated into BCI research, and sensors for wireless portable devices have been developed; however, there is still considerable work to be done. As neuroscience research has advanced, EEG signal analyses have come to require more accurate data. Due to the limited bandwidth of Bluetooth wireless transmission technology, EEG measurement systems with more than 16 channels must be used to reduce the sampling rate and prevent data loss. Therefore, the goal of this study was to develop a multichannel, high-resolution (24-bit), high-sampling-rate EEG BCI device that transmits signals via Wi-Fi. We believe that this system can be used in neuroscience research. The EEG acquisition system proposed in this work is based on a Cortex-M4 microcontroller with a Wi-Fi subsystem, providing a multichannel design and improved signal quality. This system is compatible with wet sensors, Ag/AgCl electrodes, and dry sensors. A LabVIEW-based user interface receives EEG data via Wi-Fi transmission and saves the raw data for offline analysis. In previous cognitive experiments, event tags have been communicated using Recommended Standard 232 (RS-232). The developed system was validated through event-related potential (ERP) and steady-state visually evoked potential (SSVEP) experiments. Our experimental results demonstrate that this system is suitable for recording EEG measurements and has potential in practical applications. The advantages of the developed system include its high sampling rate and high amplification. Additionally, in the future, Internet of Things (IoT) technology can be integrated into the system for remote real-time analysis or edge computing.

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http://hdl.handle.net/10453/176109