title: Detecting voluntary gait intention of chronic stroke patients towards top-down gait rehabilitation using EEG.
abstract: One of the recent trends in gait rehabilitation is to incorporate bio-signals, such as electromyography (EMG) or electroencephalography (EEG), for facilitating neuroplasticity, i.e. top-down approach. In this study, we investigated decoding stroke patients' gait intention through a wireless EEG system. To overcome patient-specific EEG patterns due to impaired cerebral cortices, common spatial patterns (CSP) was employed. We demonstrated that CSP filter can be used to maximize the EEG signal variance-ratio of gait and standing conditions. Finally, linear discriminant analysis (LDA) classification was conducted, whereby the average accuracy of 73.2% and the average delay of 0.13 s were achieved for 3 chronic stroke patients. Additionally, we also found out that the inverse CSP matrix topography of stroke patients' EEG showed good agreement with the patients' paretic side.
authors.lastname: Junhyuk Choi
authors.lastname: Hyolim Kang
authors.lastname: Sang Hun Chung
authors.lastname: Yeonghun Kim
authors.lastname: Ung Hee Lee
authors.lastname: Jong Min Lee
authors.lastname: Seung-Jong Kim
authors.lastname: Min Ho Chun
authors.lastname: Hyungmin Kim
mesh_terms: D004569:Electroencephalography; D004576:Electromyography; D005684:Gait; D006801:Humans; D033182:Intention; D020521:Stroke; D000071939:Stroke Rehabilitation