Identificador persistente para citar o vincular este elemento:
http://hdl.handle.net/10553/133373
Título: | A Multi-view Spatio-Temporal EEG Feature Learning for Cross-Subject Motor Imagery Classification | Autores/as: | Hameed, Adel Fourati, Rahma Ammar, Boudour Sanchez-Medina, Javier J. Ltifi, Hela |
Clasificación UNESCO: | 3314 Tecnología médica | Palabras clave: | Electroencephalography Focal Modulation Networks Motor Imagery Multi-View Representation |
Fecha de publicación: | 2024 | Publicación seriada: | Communications in Computer and Information Science | Conferencia: | 16th International Conference on Computational Collective Intelligence (ICCCI 2024) | Resumen: | This study introduces MV-FocalNet, a novel approach for classifying motor imagery from electroencephalography (EEG) signals. MV-FocalNet leverages multi-view representation learning and spatial-temporal modeling to extract diverse properties from multiple frequency bands of EEG data. By integrating information from multiple perspectives, MV-FocalNet captures both local and global features, significantly enhancing the accuracy of motor imagery task classification. Experimental results on two EEG datasets, 2a and 2b, show that MV-FocalNet accurately categorizes various motor movements, including left and right-hand activities, foot motions, and tongue actions. The proposed method outperforms existing state-of-the-art models, achieving substantial improvements in classification accuracy. | URI: | http://hdl.handle.net/10553/133373 | ISBN: | 9783031702587 | ISSN: | 1865-0929 | DOI: | 10.1007/978-3-031-70259-4_30 | Fuente: | Communications in Computer and Information Science[ISSN 1865-0929],v. 2166 CCIS, p. 393-405, (Enero 2024) |
Colección: | Actas de congresos |
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