Developing a Motor Imagery-Based Real-Time Asynchronous Hybrid BCI Controller for a Lower-Limb Exoskeleton

• Published in: Sensors (Basel, Switzerland) Vol. 20; no. 24; p. 7309
• Main Authors: Choi, Junhyuk, Kim, Keun Tae, Jeong, Ji Hyeok, Kim, Laehyun, Lee, Song Joo, Kim, Hyungmin
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI 19.12.2020; MDPI AG
• Subjects: Brain-Computer Interfaces; EEG; Electroencephalography; Exoskeleton Device; FBCSP; Humans; hybrid BCI; Imagination; lower-limb exoskeleton; motor imagery; Support Vector Machine; lower-limb exoskeleton; hybrid BCI; motor imagery; FBCSP; EEG
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s20247309

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.