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dc.contributor.authorCao, Lei-
dc.contributor.authorWang, Wenrong-
dc.contributor.authorHuang, Chenxi-
dc.contributor.authorXu, Zhixiong-
dc.contributor.authorWang, Han-
dc.contributor.authorJia, Jie-
dc.contributor.authorChen, Shugeng-
dc.contributor.authorDong, Yilin-
dc.contributor.authorFan, Chunjiang-
dc.contributor.authorAlbuquerque, Victor Hugo Costa de-
dc.date.accessioned2023-07-11T14:14:50Z-
dc.date.available2023-07-11T14:14:50Z-
dc.date.issued2022-
dc.identifier.citationCAO, 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.pt_BR
dc.identifier.issn1558-0210-
dc.identifier.otherDOI: https://doi.org/10.1109/TNSRE.2022.3198434-
dc.identifier.urihttp://www.repositorio.ufc.br/handle/riufc/73443-
dc.description.abstractMotor-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.pt_BR
dc.language.isoenpt_BR
dc.publisherIEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERINGpt_BR
dc.rightsAcesso Abertopt_BR
dc.subjectBCIpt_BR
dc.subjectConnectivity network analysispt_BR
dc.subjectRehabilitationpt_BR
dc.subjectStrokept_BR
dc.subjectEmporal-spatial analysispt_BR
dc.subjectAnálise de rede de conectividadept_BR
dc.subjectReabilitaçãopt_BR
dc.subjectAVCpt_BR
dc.subjectAnálise emporo-espacialpt_BR
dc.titleAn effective fusing approach by combining connectivity network pattern and temporal-spatial analysis for EEG- based BCI rehabilitationpt_BR
dc.typeArtigo de Periódicopt_BR
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