Exploration of motion-associated information from EEG during upper limb movement
Authors: 
Jue Gao, Chi Zhu, Peirang Li, Naoya Ueda, Shuto Fukushima, Yusuke KimuraAuthors Info & Claims
Pages 85 - 93
Abstract
Upper limb exoskeleton power-assisted system is dedicated to assisting disabled or vulnerable groups to perform activities of daily living, where the primary object is to differentiate EEG signals that correspond to motion and idle states. This paper proposed a novel method for detecting flexion-related peaks and extension-related peaks autonomously. First, EEG data are analyzed to understand signal characteristics and establish baseline distribution models for individual subjects. Then, the detection threshold is derived by the baseline power estimation, and a sliding window is employed to eliminate high-amplitude random noise. The windowed average EEG signal is compared with the threshold to determine whether it corresponds to motion. This method is applied to EEG data collected during shoulder and elbow movement experiments, and the results are encouraging, with accurate detection of flexion-related peaks and extension-related peaks in most cases. These findings can aid in labeling motion processes in EEG, enabling the correlation of EEG signals with control commands for power assistance.
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Index Terms
- Exploration of motion-associated information from EEG during upper limb movement
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Published In
September 2023
226 pages
ISBN:9798400708152
DOI:10.1145/3632047
Copyright © 2023 ACM.
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Published: 27 February 2024
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ICBRA 2023
ICBRA 2023: 2023 the 10th International Conference on Bioinformatics Research and Application
September 22 - 24, 2023
Barcelona, Spain
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