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• W3115777743 abstract "This study aimed to develop an intuitive gait-related motor imagery (MI)-based hybrid brain-computer interface (BCI) controller for a lower-limb exoskeleton and investigate the feasibility of the controller under a practical scenario including stand-up, gait-forward, and sit-down. A filter bank common spatial pattern (FBCSP) and mutual information-based best individual feature (MIBIF) selection were used in the study to decode MI electroencephalogram (EEG) signals and extract a feature matrix as an input to the support vector machine (SVM) classifier. A successive eye-blink switch was sequentially combined with the EEG decoder in operating the lower-limb exoskeleton. Ten subjects demonstrated more than 80% accuracy in both offline (training) and online. All subjects successfully completed a gait task by wearing the lower-limb exoskeleton through the developed real-time BCI controller. The BCI controller achieved a time ratio of 1.45 compared with a manual smartwatch controller. The developed system can potentially be benefit people with neurological disorders who may have difficulties operating manual control." @default.
• W3115777743 created "2021-01-05" @default.
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• W3115777743 date "2020-12-19" @default.
• W3115777743 modified "2023-10-10" @default.
• W3115777743 title "Developing a Motor Imagery-Based Real-Time Asynchronous Hybrid BCI Controller for a Lower-Limb Exoskeleton" @default.
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• W3115777743 doi "https://doi.org/10.3390/s20247309" @default.
• W3115777743 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7766128" @default.
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• W3115777743 hasPublicationYear "2020" @default.
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