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EEG analysis of mixed‐reality music rehabilitation system for post‐stroke lower limb therapy
Author(s) -
Chang WeiChiao,
Ko LiWei,
Yu KuenHan,
Ho YuChun,
Chen ChiaHsin,
Jong YuhJyh,
Huang YiPai
Publication year - 2019
Publication title -
journal of the society for information display
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 52
eISSN - 1938-3657
pISSN - 1071-0922
DOI - 10.1002/jsid.787
Subject(s) - rehabilitation , physical medicine and rehabilitation , task (project management) , electroencephalography , stroke (engine) , gait , medicine , computer science , psychology , physical therapy , neuroscience , mechanical engineering , management , engineering , economics
Lots of evidence and guidelines recommended that stroke patients have to do the rehabilitation all the time, but in fact, the ratio of patients doing the rehabilitation is usually less than one third. In order to enhance the rehabilitation efficacy, we develop an innovative mixed‐reality music rehabilitation (MR 2 ) system, which is consisted of an MR goggle, inertial measurement unit sensors, and an EEG system. Several music contents with different levels are implemented into the MR system. While doing the rehabilitation task, our system can monitor patient's both gait information and electroencephalographic (EEG) signals to understand the rehabilitation performance in both central and peripheral nervous systems. The MR 2 system has been pilot testing on two stroke patients and three healthy controls. Experiment results show that the patient's motor function is significantly activating when wearing the MR 2 system during the rehabilitation task. Furthermore, the gait analysis results also show that flexion angle of the hemiplegic knee during walking was significantly improved when following the tempo of the MR music content in the rehabilitation. The pilot testing results provide new insights into the understanding of complex brain functions of patients actively and continuously performing the rehabilitation ordinary tasks within the mixed‐reality applications.