EEG generalizable representations learning via masked fractional fourier domain modeling

Deep learning methods currently represent the state-of-the-art (SOTA) in electroencephalography (EEG) decoding, primarily focusing on the development of supervised models. However, most supervised methods are task-specific and lack the ability to generate generalizable latent features for use across...

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Veröffentlicht in:	Applied soft computing Jg. 170; S. 112731
Hauptverfasser:	Zhang, Shubin, An, Dong, Liu, Jincun, Wei, Yaoguang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.02.2025
Schlagworte:	Fractional fourier transform (frFT) Masked autoencoder (MAE) Motor imagery (MI) Self-supervised learning (SSL) Steady-state visual evoked potential (SSVEP) Self-supervised learning (SSL) Motor imagery (MI) Masked autoencoder (MAE) Steady-state visual evoked potential (SSVEP) Fractional fourier transform (frFT)
ISSN:	1568-4946
Online-Zugang:	Volltext
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