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.
Artigo
em Chinês
| WPRIM
| ID: wpr-888202
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.
Texto completo:
DisponíveL
Índice:
WPRIM (Pacífico Ocidental)
Assunto principal:
Algoritmos
/
Encéfalo
/
Análise Discriminante
/
Eletroencefalografia
/
Máquina de Vetores de Suporte
/
Interfaces Cérebro-Computador
Tipo de estudo:
Guia de Prática Clínica
Idioma:
Chinês
Revista:
Journal of Biomedical Engineering
Ano de publicação:
2021
Tipo de documento:
Artigo
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