Classification algorithms of error-related potentials in brain-computer interface / 生物医学工程学杂志
Journal of Biomedical Engineering
; (6): 463-472, 2021.
Article
en Zh
| WPRIM
| ID: wpr-888202
Biblioteca responsable:
WPRO
ABSTRACT
Error self-detection based on error-related potentials (ErrP) is promising to improve the practicability of brain-computer interface systems. But the single trial recognition of ErrP is still a challenge that hinters the development of this technology. To assess the performance of different algorithms on decoding ErrP, this paper test four kinds of linear discriminant analysis algorithms, two kinds of support vector machines, logistic regression, and discriminative canonical pattern matching (DCPM) on two open accessed datasets. All algorithms were evaluated by their classification accuracies and their generalization ability on different sizes of training sets. The study results show that DCPM has the best performance. This study shows a comprehensive comparison of different algorithms on ErrP classification, which could give guidance for the selection of ErrP algorithm.
Palabras clave
Texto completo:
1
Base de datos:
WPRIM
Asunto principal:
Algoritmos
/
Encéfalo
/
Análisis Discriminante
/
Electroencefalografía
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Máquina de Vectores de Soporte
/
Interfaces Cerebro-Computador
Tipo de estudio:
Guideline
Idioma:
Zh
Revista:
Journal of Biomedical Engineering
Año:
2021
Tipo del documento:
Article