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Investigating the modulation of brain activity associated with handgrip force and fatigue

Issue title: Papers from the 3rd International Conference on Biomedical Engineering and Technology (iCBEB 2014), 25–28 September 2014, Beijing, China

Subtitle:

Guest editors: Edward J. Ciaccio and Edward J. Ciaccio

Article type: Research Article

Authors: Cao, Liua | Hao, Dongmeia; * | Rong, Yaoa; b | Zhou, Yanana | Li, Mingaia | Tian, Yunqingc

Affiliations: [a] College of Life and Bioengineering, Beijing University of Technology, Beijing, China | [b] Medical Engineering Division, Xuanwu Hospital, Capital Medical University, Beijing, China | [c] State Intellectual Property Office of the PRC, Beijing, China

Correspondence: [*] Corresponding author: Dongmei Hao, College of life and Bioengineering, Beijing University of Technology, Beijing 100124, China. Tel.: +86 13520385851; E-mail:haodongmei@bjut.edu.cn

Keywords: Electroencephalography (EEG), maximal voluntary contractions (MVC), EEG power, brain activity, handgrip force

DOI: 10.3233/THC-150979

Journal: Technology and Health Care, vol. 23, no. s2, pp. S427-S433, 2015

Published: 2015
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Abstract

OBJECTIVE: To investigate the effect of force level and fatigue on brain activity during handgrip tasks.

METHODS: Electroencephalography (EEG) signals were recorded from eleven healthy male subjects when they performed 25%, 50% and 75% maximal voluntary contraction (MVC), and were in fatigue state. EEG powers in different handgrip tasks were analyzed in the frequency domain and time domain respectively.

RESULTS: The EEG power at 25%MVC was significantly lower than that at 75%MVC in gamma band (p< 0.05) for electrode C3, C4, Cz, Pz and Fz. EEG power at 25%MVC was also significantly lower than that at 75%MVC in beta band (p< 0.05) for electrode C3. However, the handgrip force level and fatigue did not affect the EEG powers for the other frequencies and electrodes (p> 0.05).

CONCLUSION: The results suggest that handgrip force level may modulate the brain activity in certain frequency bands and cortical regions. EEG power is a useful tool to characterize the motor state.

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