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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