Monitoring Stroke Rehabilitation Re-Learning Program using EEG Parameter: A preliminary study for developing self-monitoring system for stroke rehabilitation during new normal

Abstract

In the new normal, a period after Covid-19 outbreak, many things run in the new normal. Including stroke rehabilitation. During the Covid-19 and new normal era, stroke patients are not allowed to gather in a hospital in queue line for rehabilitation service. A new approach is needed to keep the rehabilitation running with a big caution to Covid-19. EEG is an alternative technology for supporting the self-monitoring stroke rehabilitation. In this study, EEG parameters such as mean, Standard deviation, mean absolute value were analyzed and tested to answer our hypotheses whether or not those parameters can be used for monitoring stroke rehabilitation progress. This study involved 3 stroke patients who underwent stroke rehabilitation using re-learning program. Each time stroke patient performed rehabilitation program EEG data was recorded. During two months measurement in total from 3 stroke patients, 12 set EEG data was obtained and analyzed. Two motions were recorded namely hand movements and elbow movements. C3 and C4 EEG channel are used to get the raw EEG data. Data processing such as filtering EEG band into alpha and beta band, noise artefact removal (ICA) and data calculation were done before obtaining the monitoring parameters. The result showed that during post stroke rehabilitation parameters such as Mean, Standard Deviation and Mean Absolute Value showed higher value in both EEG band, alpha and beta. In conclusion, EEG statistical parameters can be used as a monitoring parameter during stroke rehabilitation. In the era of new normal, this could be a solution for home care stroke rehabilitation program.