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...

Celý popis

Podrobná bibliografia

Vydané v:	Applied soft computing Ročník 170; s. 112731
Hlavní autori:	Zhang, Shubin, An, Dong, Liu, Jincun, Wei, Yaoguang
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier B.V 01.02.2025
Predmet:	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
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!