Motor Imagery Observed by fNIRS

Chi Sang Choy; Zixin Ye; Ziyang Huang; Qifeng Zheng; Qiang Fang; Seedahmed S. Mahmoud; Katrina Neville; Beth Jelfs

doi:10.1109/BSN58485.2023.10331069

Motor Imagery Observed by fNIRS

Chi Sang Choy^*, Zixin Ye, Ziyang Huang, Qifeng Zheng, Qiang Fang, Seedahmed S. Mahmoud, Katrina Neville, Beth Jelfs

^*Corresponding author for this work

Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Motor imagery (MI) is expected to activate brain areas related to motor functions, yet, the brain is constantly active to some extent leading to difficulties in differentiating MI from supposedly non-motor tasks. In this study, we use functional near-infrared spectroscopy (fNIRS) to offer an objective and spatially precise measurement of brain activity during MI. The fNIRS findings of this study using our MI-based exercise framework with 15 healthy subjects indicate that casual imagination and especially relaxation do not induce an intense motor brain activation compared to active MI performance. Furthermore, dynamic visual cues for MI appear to enhance brain activation around the motor brain areas of the majority of subjects and MI training helps some subjects to activate their motor cortex. Future research may refer to our framework to validate the competence of stroke patients in MI-based motor rehabilitation.

Original language	English
Title of host publication	2023 IEEE 19th International Conference on Body Sensor Networks (BSN)
Publisher	IEEE
ISBN (Electronic)	9798350338416
ISBN (Print)	9798350311983 (PoD)
DOIs	https://doi.org/10.1109/BSN58485.2023.10331069
Publication status	Published - 1 Dec 2023

Bibliographical note

Funding Information:
This work was supported in part by the Li Ka Shing Foundation [Grant No. 2020LKSFG03C], in part by the Australian Government (RTP Stipend Scholarship (RSS-SC)), in part by the RMIT University (Engineering Top-up Scholarship (E&B)).

Publisher Copyright:
© 2023 IEEE.

Keywords

Training
Brain
Visualization
Body sensor networks
Scalp
Stroke (medical condition)
Fatigue
Functional near-infrared spectroscopy
Task analysis
Signal to noise ratio

ASJC Scopus subject areas

Computer Networks and Communications
Biomedical Engineering
Health Informatics
Instrumentation

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10.1109/BSN58485.2023.10331069

Cite this

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title = "Motor Imagery Observed by fNIRS",

abstract = "Motor imagery (MI) is expected to activate brain areas related to motor functions, yet, the brain is constantly active to some extent leading to difficulties in differentiating MI from supposedly non-motor tasks. In this study, we use functional near-infrared spectroscopy (fNIRS) to offer an objective and spatially precise measurement of brain activity during MI. The fNIRS findings of this study using our MI-based exercise framework with 15 healthy subjects indicate that casual imagination and especially relaxation do not induce an intense motor brain activation compared to active MI performance. Furthermore, dynamic visual cues for MI appear to enhance brain activation around the motor brain areas of the majority of subjects and MI training helps some subjects to activate their motor cortex. Future research may refer to our framework to validate the competence of stroke patients in MI-based motor rehabilitation.",

keywords = "Training, Brain, Visualization, Body sensor networks, Scalp, Stroke (medical condition), Fatigue, Functional near-infrared spectroscopy, Task analysis, Signal to noise ratio",

author = "Choy, {Chi Sang} and Zixin Ye and Ziyang Huang and Qifeng Zheng and Qiang Fang and Mahmoud, {Seedahmed S.} and Katrina Neville and Beth Jelfs",

note = "Funding Information: This work was supported in part by the Li Ka Shing Foundation [Grant No. 2020LKSFG03C], in part by the Australian Government (RTP Stipend Scholarship (RSS-SC)), in part by the RMIT University (Engineering Top-up Scholarship (E&B)). Publisher Copyright: {\textcopyright} 2023 IEEE.",

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day = "1",

doi = "10.1109/BSN58485.2023.10331069",

language = "English",

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AU - Choy, Chi Sang

AU - Ye, Zixin

AU - Huang, Ziyang

AU - Zheng, Qifeng

AU - Fang, Qiang

AU - Mahmoud, Seedahmed S.

AU - Neville, Katrina

AU - Jelfs, Beth

N1 - Funding Information: This work was supported in part by the Li Ka Shing Foundation [Grant No. 2020LKSFG03C], in part by the Australian Government (RTP Stipend Scholarship (RSS-SC)), in part by the RMIT University (Engineering Top-up Scholarship (E&B)). Publisher Copyright: © 2023 IEEE.

PY - 2023/12/1

Y1 - 2023/12/1

N2 - Motor imagery (MI) is expected to activate brain areas related to motor functions, yet, the brain is constantly active to some extent leading to difficulties in differentiating MI from supposedly non-motor tasks. In this study, we use functional near-infrared spectroscopy (fNIRS) to offer an objective and spatially precise measurement of brain activity during MI. The fNIRS findings of this study using our MI-based exercise framework with 15 healthy subjects indicate that casual imagination and especially relaxation do not induce an intense motor brain activation compared to active MI performance. Furthermore, dynamic visual cues for MI appear to enhance brain activation around the motor brain areas of the majority of subjects and MI training helps some subjects to activate their motor cortex. Future research may refer to our framework to validate the competence of stroke patients in MI-based motor rehabilitation.

AB - Motor imagery (MI) is expected to activate brain areas related to motor functions, yet, the brain is constantly active to some extent leading to difficulties in differentiating MI from supposedly non-motor tasks. In this study, we use functional near-infrared spectroscopy (fNIRS) to offer an objective and spatially precise measurement of brain activity during MI. The fNIRS findings of this study using our MI-based exercise framework with 15 healthy subjects indicate that casual imagination and especially relaxation do not induce an intense motor brain activation compared to active MI performance. Furthermore, dynamic visual cues for MI appear to enhance brain activation around the motor brain areas of the majority of subjects and MI training helps some subjects to activate their motor cortex. Future research may refer to our framework to validate the competence of stroke patients in MI-based motor rehabilitation.

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KW - Visualization

KW - Body sensor networks

KW - Scalp

KW - Stroke (medical condition)

KW - Fatigue

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KW - Task analysis

KW - Signal to noise ratio

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DO - 10.1109/BSN58485.2023.10331069

M3 - Conference contribution

AN - SCOPUS:85181588412

SN - 9798350311983 (PoD)

BT - 2023 IEEE 19th International Conference on Body Sensor Networks (BSN)

PB - IEEE

ER -

Motor Imagery Observed by fNIRS

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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