Enhancing the performance of SSVEP-based BCIs by combining task-related component analysis and deep neural network

Enhancing the performance of SSVEP-based BCIs by combining task-related component analysis and deep neural network

Abstract Steady-State Visually Evoked Potential (SSVEP) signals can be decoded by either a traditional machine learning algorithm or a deep learning network. Combining the two methods is expected to enhance the performance of an SSVEP-based brain-computer interface (BCI) by exploiting their advantag...

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Bibliographic Details
Main Authors: Qingguo Wei, Chang Li, Yijun Wang, Xiaorong Gao
Format: Article
Language:English
Published: Nature Portfolio 2025-01-01
Series:Scientific Reports
Subjects:
Brain-computer interface
Steady-state visual evoked potential
Ensemble task-related component analysis
Sub-band convolutional neural network
Model combination
Online Access:https://doi.org/10.1038/s41598-024-84534-6
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https://doi.org/10.1038/s41598-024-84534-6

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