Utilize este identificador para referenciar este registo:
https://hdl.handle.net/10316/101896
Título: | Improving the Classifier Performance in Motor Imagery Task Classification: What are the steps in the classification process that we should worry about? | Autor: | García-Laencina, Pedro J. Germán, Rodríguez-Bermúdez Abreu, Pedro Henriques Santos, Miriam Seoane |
Palavras-chave: | Brain Computer Interface Systems; Motor Imagery Tasks; Pattern Recognition; Machine Learning | Data: | 2018 | Título da revista, periódico, livro ou evento: | International Journal of Computational Intelligence Systems | Volume: | 11 | Número: | 1 | Resumo: | Brain-Computer Interface systems based on motor imagery are able to identify an individual’s intent to initiate control through the classification of encephalography patterns. Correctly classifying such patterns is instrumental and strongly depends in a robust machine learning block that is able to properly process the features extracted from a subject’s encephalograms. The main objective of this work is to provide an overall view on machine learning stages, aiming to answer the following question: “What are the steps in the classification process that we should worry about?”. The obtained results suggest that future research in the field should focus on two main aspects: exploring techniques for dimensionality reduction, in particular, supervised linear approaches, and evaluating adequate validation schemes to allow a more precise interpretation of results. | URI: | https://hdl.handle.net/10316/101896 | ISSN: | 1875-6883 | DOI: | 10.2991/ijcis.11.1.95 | Direitos: | openAccess |
Aparece nas coleções: | I&D CISUC - Artigos em Revistas Internacionais |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
---|---|---|---|---|
25902766.pdf | 3.39 MB | Adobe PDF | Ver/Abrir |
Citações SCOPUSTM
2
Visto em 17/nov/2022
Citações WEB OF SCIENCETM
2
Visto em 15/nov/2022
Visualizações de página
81
Visto em 17/jul/2024
Downloads
28
Visto em 17/jul/2024
Google ScholarTM
Verificar
Altmetric
Altmetric
Este registo está protegido por Licença Creative Commons