Classification of hemodynamic responses associated with force and speed imagery for a brain-computer interface.
J Med Syst
; 39(5): 53, 2015 May.
Article
em En
| MEDLINE
| ID: mdl-25732084
ABSTRACT
Functional near-infrared spectroscopy (fNIRS) is an emerging optical technique, which can assess brain activities associated with tasks. In this study, six participants were asked to perform three imageries of hand clenching associated with force and speed, respectively. Joint mutual information (JMI) criterion was used to extract the optimal features of hemodynamic responses. And extreme learning machine (ELM) was employed to be the classifier. ELM solved the major bottleneck of feedforward neural networks in learning speed, this classifier was easily implemented and less sensitive to specified parameters. The 2-class fNIRS-BCI system was firstly built with an average accuracy of 76.7%, when all force and speed tasks were categorized as one class, respectively. The multi-class systems based on different levels of force and speed attempted to be investigated, the accuracies were moderate. This study provided a novel paradigm for establishing fNIRS-BCI system, and provided a possibility to produce more degrees of freedom in BCI system.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Interfaces Cérebro-Computador
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Aprendizado de Máquina
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Hemodinâmica
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Imaginação
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Córtex Motor
Tipo de estudo:
Risk_factors_studies
Limite:
Adult
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Female
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Humans
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Male
Idioma:
En
Revista:
J Med Syst
Ano de publicação:
2015
Tipo de documento:
Article