Trans-cVAE-GAN: Transformer-Based cVAE-GAN for High-Fidelity EEG Signal Generation

Electroencephalography signal generation remains a challenging task due to its non-stationarity, multi-scale oscillations, and strong spatiotemporal coupling. Conventional generative models, including VAEs and GAN variants such as DCGAN, WGAN, and WGAN-GP, often yield blurred waveforms, unstable spe...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Bioengineering (Basel) Jg. 12; H. 10; S. 1028
Hauptverfasser:	Yao, Yiduo, Wang, Xiao, Hao, Xudong, Sun, Hongyu, Dong, Ruixin, Li, Yansheng
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Switzerland MDPI AG 26.09.2025
Schlagworte:	Ablation Affective computing Biochips Brain Comparative analysis Computer applications conditional variational autoencoder Conditioning Control stability Controllability Design EEG Electric transformers Electroencephalography Emotion recognition Emotions Fourier transforms generative adversarial network Generative adversarial networks generative modeling Human-computer interface Implants Labels Liquors Machine learning Measurement techniques Neural networks Neurophysiology Oscillations Realism Semantics Signal generation Time series transformer Waveforms Wavelet transforms generative adversarial network conditional variational autoencoder transformer generative modeling
ISSN:	2306-5354, 2306-5354
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!