TY - GEN
T1 - Brain-muscle Interaction Analysis with Time-variant Granger Causality
AU - Tun, Nyi Nyi
AU - Sanuki, Fumiya
AU - Iramina, Keiji
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85181587472&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85181587472&partnerID=8YFLogxK
U2 - 10.1109/BSN58485.2023.10330913
DO - 10.1109/BSN58485.2023.10330913
M3 - Conference contribution
AN - SCOPUS:85181587472
T3 - 2023 IEEE 19th International Conference on Body Sensor Networks, BSN 2023 - Proceedings
BT - 2023 IEEE 19th International Conference on Body Sensor Networks, BSN 2023 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 19th IEEE International Conference on Body Sensor Networks, BSN 2023
Y2 - 9 October 2023 through 11 October 2023
ER -