Study of on-line adaptive discriminant analysis for EEG-based brain computer interfaces.
IEEE Trans Biomed Eng
; 54(3): 550-6, 2007 Mar.
Article
en En
| MEDLINE
| ID: mdl-17355071
ABSTRACT
A study of different on-line adaptive classifiers, using various feature types is presented. Motor imagery brain computer interface (BCI) experiments were carried out with 18 naive able-bodied subjects. Experiments were done with three two-class, cue-based, electroencephalogram (EEG)-based systems. Two continuously adaptive classifiers were tested adaptive quadratic and linear discriminant analysis. Three feature types were analyzed, adaptive autoregressive parameters, logarithmic band power estimates and the concatenation of both. Results show that all systems are stable and that the concatenation of features with continuously adaptive linear discriminant analysis classifier is the best choice of all. Also, a comparison of the latter with a discontinuously updated linear discriminant analysis, carried out in on-line experiments with six subjects, showed that on-line adaptation performed significantly better than a discontinuous update. Finally a static subject-specific baseline was also provided and used to compare performance measurements of both types of adaptation.
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Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Interfaz Usuario-Computador
/
Encéfalo
/
Reconocimiento de Normas Patrones Automatizadas
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Inteligencia Artificial
/
Potenciales Evocados Motores
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Electroencefalografía
/
Imaginación
Tipo de estudio:
Evaluation_studies
/
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
IEEE Trans Biomed Eng
Año:
2007
Tipo del documento:
Article
País de afiliación:
España