Abstract
EEG has proved to be a vital tool in diagnosing stroke. Now a days EEG is also used for tracking the rehabilitation process after stroke. Post stroke subjects are guided to do some physical activities to help them in regaining lost motor activity. The study presented here acquires EEG data from patients undergoing rehabilitation process and tracks the improvement in their motor ability. This is done by correlating change in Fugl-Meyer Assessment (FMA) score with some of the features obtained from EEG data. A significant correlation was found between FMA change and mean absolute value (r = 0.6, p < 0.001) of the EEG signal and also between change in mean alpha power ratio (left/right) vs. \(\varDelta \)FMA (r = −0.71, p < 0.001). The high correlation values of these features suggest that they can be used for monitoring rehabilitation after stroke.
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Acknowledgements
This study is supported by the MHRD, Govt. of India, Dept. of Higher Education, Shastri Bhawan, New Delhi, and Indian Council of Medical Research, Dept. of Health Research, Ministry of Health and Family Welfare, Ansari Nagar, New Delhi - 110 029. (No: IIT/SRIC/SMST/RHU/2016-17/250 Dated: 10-04-2017)
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Singh, S. et al. (2020). Monitoring Post-stroke Motor Rehabilitation Using EEG Analysis. In: Tiwary, U., Chaudhury, S. (eds) Intelligent Human Computer Interaction. IHCI 2019. Lecture Notes in Computer Science(), vol 11886. Springer, Cham. https://doi.org/10.1007/978-3-030-44689-5_2
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