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http://repositorio.ufc.br/handle/riufc/73443
Tipo: | Artigo de Periódico |
Título: | An effective fusing approach by combining connectivity network pattern and temporal-spatial analysis for EEG- based BCI rehabilitation |
Autor(es): | Cao, Lei Wang, Wenrong Huang, Chenxi Xu, Zhixiong Wang, Han Jia, Jie Chen, Shugeng Dong, Yilin Fan, Chunjiang Albuquerque, Victor Hugo Costa de |
Palavras-chave: | BCI;Connectivity network analysis;Rehabilitation;Stroke;Emporal-spatial analysis;Análise de rede de conectividade;Reabilitação;AVC;Análise emporo-espacial |
Data do documento: | 2022 |
Instituição/Editor/Publicador: | IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING |
Citação: | CAO, Lei; WANG, Wenrong; XU, Zhixiong; WANG, Han; JIA, Jie; CHEN, Shugeng; DONG, Yilin; FAN, Chunjiang; ALBUQUERQUE, Victor Hugo Costa de. An effective fusing approach by combining connectivity network pattern and temporal-spatial analysis for EEG- based BCI rehabilitation. IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, [s.l.], v. 30, p. 2264-2274, 2022. |
Abstract: | Motor-modality-based brain computer interface (BCI) could promote the neural rehabilitation for stroke patients. Temporal-spatial analysis was commonly used for pattern recognition in this task. This paper introduced a novel connectivity network analysis for EEG-based feature selection. The network features of connectivity pattern not only captured the spatial activities responding to motor task, but also mined the interactive pattern among these cerebral regions. Furthermore, the effective combination between temporal-spatial analysis and network analysis was evaluated for improving the performance of BCI classification (81.7%). And the results demonstrated that it could raise the classification accuracies for most of patients (6 of 7 patients). This proposed method was meaningful for developing the effective BCI training program for stroke rehabilitation. |
URI: | http://www.repositorio.ufc.br/handle/riufc/73443 |
ISSN: | 1558-0210 |
Tipo de Acesso: | Acesso Aberto |
Aparece nas coleções: | DEEL - Artigos publicados em revista científica |
Arquivos associados a este item:
Arquivo | Descrição | Tamanho | Formato | |
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2022_art_lcao.pdf | 3,05 MB | Adobe PDF | Visualizar/Abrir |
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