Brain-muscle Interaction Analysis with Time-variant Granger Causality

Nyi Nyi Tun, Fumiya Sanuki, Keiji Iramina

研究成果: 書籍/レポート タイプへの寄稿会議への寄与

抄録

This study uses time-variant Granger causality to calculate the amount of functional interaction with the inference of information flow direction. Four different motor tasks were taken into consideration. They are real movement (RM), movement intention (Inten), motor imagery (MI), and only looking at the virtual hand in three-dimensional head-mounted display (OL) tasks. For the purpose of task instructions, we designed the experimental tasks in a 3D-HMD virtual reality environment. Examining the causality between two different biological signals is still challenging, and there have been few studies of causality between brain and muscle signals. Thus, the main aim of this study is to proclaim that time-variant Granger causality is an easy-To-Apply and effective method for inferring information flow direction between ascending and descending pathways of brain and muscle signals. Generally, our final results strongly proved that brain-muscle functional interaction changes according to the motor tasks executed. Furthermore, high functional interaction appears in RM, Inten and MI tasks (in some subjects) rather than OL task in both afferent and efferent directions. Among many functional interaction methods, time-variant Granger causality is one of the most basic and reliable methods for investigating two different neurophysiological signals, such as EEG and EMG, to calculate the direction of information.

本文言語英語
ホスト出版物のタイトル2023 IEEE 19th International Conference on Body Sensor Networks, BSN 2023 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9798350338416
DOI
出版ステータス出版済み - 2023
イベント19th IEEE International Conference on Body Sensor Networks, BSN 2023 - Boston, 米国
継続期間: 10月 9 202310月 11 2023

出版物シリーズ

名前2023 IEEE 19th International Conference on Body Sensor Networks, BSN 2023 - Proceedings

会議

会議19th IEEE International Conference on Body Sensor Networks, BSN 2023
国/地域米国
CityBoston
Period10/9/2310/11/23

!!!All Science Journal Classification (ASJC) codes

  • コンピュータ ネットワークおよび通信
  • 生体医工学
  • 健康情報学
  • 器械工学

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