TY - JOUR
T1 - EEG Time-Frequency Domain Analysis for Describing Healthy Subjects and Stroke Patients during Stroke Rehabilitation Motion Tasks
AU - Wibawa, Adhi Dharma
AU - Pamungkas, Yuri
AU - Pratiwi, Monica
AU - Kusumastuti, Rosita Devi
AU - Islamiyah, Wardah Rahmatul
AU - Risqiwati, Diah
N1 - Publisher Copyright:
© IJASEIT is licensed under a Creative Commons Attribution-Share Alike 4.0 International License.
PY - 2023
Y1 - 2023
N2 - To overcome the long-term impact of stroke attacks on society, stroke rehabilitation is the only solution WHO and many healthcare organizations suggested. Until recently, stroke rehabilitation monitoring has been done using visual observation, which has several drawbacks. EEG is a new approach to understanding how the central nervous system controls motion. This study compares the motion pattern done by a group of 12 healthy subjects and nine stroke patients during the rehabilitation motion tasks using the OpenBCI system. Time-frequency domain features, namely PSD, MAV, and STD are used to explore how the patterns differ. Three rehabilitation motions are implemented: grasping, elbow flexion extension, and shoulder flexion-extension. The result shows that the healthy cross-brain correlation happens in healthy subjects. This means that when the left-side arm does the motion, the EEG feature values from the right hemisphere are higher, and vice versa. However, this healthy cross-brain correlation pattern did not happen within the stroke patient group. The overall value of PSD, MAV, and STD from both hemispheres during all motions is higher in the healthy group than in stroke patients. The type of motion also contributes to describing the time-frequency domain feature comparison. In conclusion, this gap value using time-frequency domain features can be used as a target for stroke rehabilitation programs by implementing the EEG technology to monitor it.
AB - To overcome the long-term impact of stroke attacks on society, stroke rehabilitation is the only solution WHO and many healthcare organizations suggested. Until recently, stroke rehabilitation monitoring has been done using visual observation, which has several drawbacks. EEG is a new approach to understanding how the central nervous system controls motion. This study compares the motion pattern done by a group of 12 healthy subjects and nine stroke patients during the rehabilitation motion tasks using the OpenBCI system. Time-frequency domain features, namely PSD, MAV, and STD are used to explore how the patterns differ. Three rehabilitation motions are implemented: grasping, elbow flexion extension, and shoulder flexion-extension. The result shows that the healthy cross-brain correlation happens in healthy subjects. This means that when the left-side arm does the motion, the EEG feature values from the right hemisphere are higher, and vice versa. However, this healthy cross-brain correlation pattern did not happen within the stroke patient group. The overall value of PSD, MAV, and STD from both hemispheres during all motions is higher in the healthy group than in stroke patients. The type of motion also contributes to describing the time-frequency domain feature comparison. In conclusion, this gap value using time-frequency domain features can be used as a target for stroke rehabilitation programs by implementing the EEG technology to monitor it.
KW - EEG time-frequency domain
KW - MAV
KW - PSD
KW - STD
KW - Stroke rehabilitation monitoring
KW - electroencephalogram
KW - home plasticity training
UR - http://www.scopus.com/inward/record.url?scp=85158868456&partnerID=8YFLogxK
U2 - 10.18517/ijaseit.13.2.18299
DO - 10.18517/ijaseit.13.2.18299
M3 - Article
AN - SCOPUS:85158868456
SN - 2088-5334
VL - 13
SP - 666
EP - 673
JO - International Journal on Advanced Science, Engineering and Information Technology
JF - International Journal on Advanced Science, Engineering and Information Technology
IS - 2
ER -