Decoding spectro-temporal representation for motor imagery recognition using ECoG-based brain-computer interfaces.
J Integr Neurosci
; 19(2): 259-272, 2020 Jun 30.
Article
em En
| MEDLINE
| ID: mdl-32706190
ABSTRACT
One of the challenges in brain-computer interface systems is obtaining motor imagery recognition from brain activities. Brain-signal decoding robustness and system performance improvement during the motor imagery process are two of the essential issues in brain-computer interface research. In conventional approaches, ineffective decoding of features and high complexity of algorithms often lead to unsatisfactory performance. A novel method for the recognition of motor imagery tasks is developed based on employing a modified S-transforms for spectro-temporal representation to characterize the behavior of electrocorticogram activities. A classifier is trained by using a support vector machine, and an optimized wrapper approach is applied to guide selection to implement the representation selection obtained. A channel selection algorithm optimizes the wrapper approach by adding a cross-validation step, which effectively improves the classification performance. The modified S-transform can accurately capture event-related desynchronization/event-related synchronization phenomena and can effectively locate sensorimotor rhythm information. The optimized wrapper approach used in this scheme can effectively reduce the feature dimension and improve algorithm efficiency. The method is evaluated on a public electrocorticogram dataset with a recognition accuracy of 98% and an information transfer rate of 0.8586 bit/trial. To verify the effect of the channel selection, both electrocorticogram and electroencephalogram data are experimentally analyzed. Furthermore, the computational efficiency of this scheme demonstrates its potential for online brain-computer interface systems in future cognitive tasks.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Processamento de Sinais Assistido por Computador
/
Reconhecimento Automatizado de Padrão
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Córtex Cerebral
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Máquina de Vetores de Suporte
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Interfaces Cérebro-Computador
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Eletrocorticografia
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Imaginação
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Atividade Motora
Limite:
Adult
/
Humans
Idioma:
En
Revista:
J Integr Neurosci
Assunto da revista:
NEUROLOGIA
Ano de publicação:
2020
Tipo de documento:
Article