SSVEP-DAN: Cross-Domain Data Alignment for SSVEP-Based Brain-Computer Interfaces.
IEEE Trans Neural Syst Rehabil Eng
; 32: 2027-2037, 2024.
Article
en En
| MEDLINE
| ID: mdl-38781061
ABSTRACT
Steady-state visual-evoked potential (SSVEP)-based brain-computer interfaces (BCIs) offer a non-invasive means of communication through high-speed speller systems. However, their efficiency is highly dependent on individual training data acquired during time-consuming calibration sessions. To address the challenge of data insufficiency in SSVEP-based BCIs, we introduce SSVEP-DAN, the first dedicated neural network model designed to align SSVEP data across different domains, encompassing various sessions, subjects, or devices. Our experimental results demonstrate the ability of SSVEP-DAN to transform existing source SSVEP data into supplementary calibration data. This results in a significant improvement in SSVEP decoding accuracy while reducing the calibration time. We envision SSVEP-DAN playing a crucial role in future applications of high-performance SSVEP-based BCIs. The source code for this work is available at https//github.com/CECNL/SSVEP-DAN.
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Algoritmos
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Electroencefalografía
/
Potenciales Evocados Visuales
/
Interfaces Cerebro-Computador
Límite:
Adult
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Female
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Humans
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Male
Idioma:
En
Año:
2024
Tipo del documento:
Article