fNIRS-EEG BCIs for motor rehabilitation: a review
fNIRS-EEG BCIs for motor rehabilitation: a review
Motor impairment has a profound impact on a significant number of individuals, leading to a substantial demand for rehabilitation services. Through brain–computer interfaces (BCIs), people with severe motor disabilities could have improved communication with others and control appropriately designed robotic prosthetics, so as to (at least partially) restore their motor abilities. BCI plays a pivotal role in promoting smoother communication and interactions between individuals with motor impairments and others. Moreover, they enable the direct control of assistive devices through brain signals. In particular, their most significant potential lies in the realm of motor rehabilitation, where BCIs can offer real-time feedback to assist users in their training and continuously monitor the brain’s state throughout the entire rehabilitation process. Hybridization of different brain-sensing modalities, especially functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG), has shown great potential in the creation of BCIs for rehabilitating the motor-impaired populations. EEG, as a well-established methodology, can be combined with fNIRS to compensate for the inherent disadvantages and achieve higher temporal and spatial resolution. This paper reviews the recent works in hybrid fNIRS-EEG BCIs for motor rehabilitation, emphasizing the methodologies that utilized motor imagery. An overview of the BCI system and its key components was introduced, followed by an introduction to various devices, strengths and weaknesses of different signal processing techniques, and applications in neuroscience and clinical contexts. The review concludes by discussing the possible challenges and opportunities for future development.
brain–computer interface, electroencephalography, functional near-infrared spectroscopy, motor imagery, motor rehabilitation, multimodal
Chen, Jianan
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Xia, Yunjia
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Zhou, Xinkai
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Vidal Rosas, Ernesto
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Thomas, Alexander
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Loureiro, Rui
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Cooper, Robert J.
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Carlson, Tom
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Zhao, Hubin
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6 December 2023
Chen, Jianan
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Xia, Yunjia
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Zhou, Xinkai
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Vidal Rosas, Ernesto
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Thomas, Alexander
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Loureiro, Rui
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Cooper, Robert J.
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Carlson, Tom
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Zhao, Hubin
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Chen, Jianan, Xia, Yunjia, Zhou, Xinkai, Vidal Rosas, Ernesto, Thomas, Alexander, Loureiro, Rui, Cooper, Robert J., Carlson, Tom and Zhao, Hubin
(2023)
fNIRS-EEG BCIs for motor rehabilitation: a review.
Bioengineering, 10 (12), [1393].
(doi:10.3390/bioengineering10121393).
Abstract
Motor impairment has a profound impact on a significant number of individuals, leading to a substantial demand for rehabilitation services. Through brain–computer interfaces (BCIs), people with severe motor disabilities could have improved communication with others and control appropriately designed robotic prosthetics, so as to (at least partially) restore their motor abilities. BCI plays a pivotal role in promoting smoother communication and interactions between individuals with motor impairments and others. Moreover, they enable the direct control of assistive devices through brain signals. In particular, their most significant potential lies in the realm of motor rehabilitation, where BCIs can offer real-time feedback to assist users in their training and continuously monitor the brain’s state throughout the entire rehabilitation process. Hybridization of different brain-sensing modalities, especially functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG), has shown great potential in the creation of BCIs for rehabilitating the motor-impaired populations. EEG, as a well-established methodology, can be combined with fNIRS to compensate for the inherent disadvantages and achieve higher temporal and spatial resolution. This paper reviews the recent works in hybrid fNIRS-EEG BCIs for motor rehabilitation, emphasizing the methodologies that utilized motor imagery. An overview of the BCI system and its key components was introduced, followed by an introduction to various devices, strengths and weaknesses of different signal processing techniques, and applications in neuroscience and clinical contexts. The review concludes by discussing the possible challenges and opportunities for future development.
Text
bioengineering-10-01393
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Accepted/In Press date: 30 November 2023
Published date: 6 December 2023
Keywords:
brain–computer interface, electroencephalography, functional near-infrared spectroscopy, motor imagery, motor rehabilitation, multimodal
Identifiers
Local EPrints ID: 489133
URI: http://eprints.soton.ac.uk/id/eprint/489133
ISSN: 2306-5354
PURE UUID: 4cfe56ee-c4fb-4336-ac03-490d6b1f8104
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Date deposited: 15 Apr 2024 16:48
Last modified: 16 Apr 2024 02:07
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Author:
Jianan Chen
Author:
Yunjia Xia
Author:
Xinkai Zhou
Author:
Ernesto Vidal Rosas
Author:
Alexander Thomas
Author:
Rui Loureiro
Author:
Robert J. Cooper
Author:
Tom Carlson
Author:
Hubin Zhao
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