A spatial-temporal hybrid feature extraction method for rapid serial visual presentation of electroencephalogram signals / 生物医学工程学杂志
Journal of Biomedical Engineering
;
(6): 39-46, 2022.
Artigo
em Chinês
| WPRIM
| ID: wpr-928197
ABSTRACT
Rapid serial visual presentation-brain computer interface (RSVP-BCI) is the most popular technology in the early discover task based on human brain. This algorithm can obtain the rapid perception of the environment by human brain. Decoding brain state based on single-trial of multichannel electroencephalogram (EEG) recording remains a challenge due to the low signal-to-noise ratio (SNR) and nonstationary. To solve the problem of low classification accuracy of single-trial in RSVP-BCI, this paper presents a new feature extraction algorithm which uses principal component analysis (PCA) and common spatial pattern (CSP) algorithm separately in spatial domain and time domain, creating a spatial-temporal hybrid CSP-PCA (STHCP) algorithm. By maximizing the discrimination distance between target and non-target, the feature dimensionality was reduced effectively. The area under the curve (AUC) of STHCP algorithm is higher than that of the three benchmark algorithms (SWFP, CSP and PCA) by 17.9%, 22.2% and 29.2%, respectively. STHCP algorithm provides a new method for target detection.
Texto completo:
DisponíveL
Índice:
WPRIM (Pacífico Ocidental)
Assunto principal:
Algoritmos
/
Processamento de Sinais Assistido por Computador
/
Encéfalo
/
Análise de Componente Principal
/
Eletroencefalografia
/
Interfaces Cérebro-Computador
Tipo de estudo:
Estudo prognóstico
Limite:
Humanos
Idioma:
Chinês
Revista:
Journal of Biomedical Engineering
Ano de publicação:
2022
Tipo de documento:
Artigo
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