Exploiting Task Constraints for Self-Calibrated Brain-Machine Interface Control Using Error-Related Potentials.
PLoS One
; 10(7): e0131491, 2015.
Article
em En
| MEDLINE
| ID: mdl-26131890
ABSTRACT
This paper presents a new approach for self-calibration BCI for reaching tasks using error-related potentials. The proposed method exploits task constraints to simultaneously calibrate the decoder and control the device, by using a robust likelihood function and an ad-hoc planner to cope with the large uncertainty resulting from the unknown task and decoder. The method has been evaluated in closed-loop online experiments with 8 users using a previously proposed BCI protocol for reaching tasks over a grid. The results show that it is possible to have a usable BCI control from the beginning of the experiment without any prior calibration. Furthermore, comparisons with simulations and previous results obtained using standard calibration hint that both the quality of recorded signals and the performance of the system were comparable to those obtained with a standard calibration approach.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Algoritmos
/
Encéfalo
/
Potenciais Evocados
/
Interfaces Cérebro-Computador
Limite:
Adult
/
Humans
Idioma:
En
Revista:
PLoS One
Assunto da revista:
CIENCIA
/
MEDICINA
Ano de publicação:
2015
Tipo de documento:
Article
País de afiliação:
Espanha