A graphical model framework for decoding in the visual ERP-based BCI speller.
Neural Comput
; 23(1): 160-82, 2011 Jan.
Article
en En
| MEDLINE
| ID: mdl-20964540
We present a graphical model framework for decoding in the visual ERP-based speller system. The proposed framework allows researchers to build generative models from which the decoding rules are obtained in a straightforward manner. We suggest two models for generating brain signals conditioned on the stimulus events. Both models incorporate letter frequency information but assume different dependencies between brain signals and stimulus events. For both models, we derive decoding rules and perform a discriminative training. We show on real visual speller data how decoding performance improves by incorporating letter frequency information and using a more realistic graphical model for the dependencies between the brain signals and the stimulus events. Furthermore, we discuss how the standard approach to decoding can be seen as a special case of the graphical model framework. The letter also gives more insight into the discriminative approach for decoding in the visual speller system.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Corteza Visual
/
Procesamiento de Señales Asistido por Computador
/
Potenciales Evocados
/
Potenciales Evocados Visuales
/
Modelos Neurológicos
/
Modelos Teóricos
Tipo de estudio:
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Neural Comput
Asunto de la revista:
INFORMATICA MEDICA
Año:
2011
Tipo del documento:
Article
Pais de publicación:
Estados Unidos