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Dual-Alpha: a large EEG study for dual-frequency SSVEP brain-computer interface.
Sun, Yike; Liang, Liyan; Li, Yuhan; Chen, Xiaogang; Gao, Xiaorong.
Afiliación
  • Sun Y; The School of Biomedical Engineering, Tsinghua University, Beijing 100084, China.
  • Liang L; The China Academy of Information and Communications Technology, Beijing 100191, China.
  • Li Y; Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin 300192, China.
  • Chen X; The School of Life Sciences, Tiangong University, Tianjin 300387, China.
  • Gao X; Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin 300192, China.
Gigascience ; 132024 Jan 02.
Article en En | MEDLINE | ID: mdl-39110623
ABSTRACT

BACKGROUND:

The domain of brain-computer interface (BCI) technology has experienced significant expansion in recent years. However, the field continues to face a pivotal challenge due to the dearth of high-quality datasets. This lack of robust datasets serves as a bottleneck, constraining the progression of algorithmic innovations and, by extension, the maturation of the BCI field.

FINDINGS:

This study details the acquisition and compilation of electroencephalogram data across 3 distinct dual-frequency steady-state visual evoked potential (SSVEP) paradigms, encompassing over 100 participants. Each experimental condition featured 40 individual targets with 5 repetitions per target, culminating in a comprehensive dataset consisting of 21,000 trials of dual-frequency SSVEP recordings. We performed an exhaustive validation of the dataset through signal-to-noise ratio analyses and task-related component analysis, thereby substantiating its reliability and effectiveness for classification tasks.

CONCLUSIONS:

The extensive dataset presented is set to be a catalyst for the accelerated development of BCI technologies. Its significance extends beyond the BCI sphere and holds considerable promise for propelling research in psychology and neuroscience. The dataset is particularly invaluable for discerning the complex dynamics of binocular visual resource distribution.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Electroencefalografía / Potenciales Evocados Visuales / Interfaces Cerebro-Computador Límite: Adult / Female / Humans / Male Idioma: En Revista: Gigascience Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Electroencefalografía / Potenciales Evocados Visuales / Interfaces Cerebro-Computador Límite: Adult / Female / Humans / Male Idioma: En Revista: Gigascience Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos