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...

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Bibliographic Details
Published in:	Bioengineering (Basel) Vol. 12; no. 10; p. 1028
Main Authors:	Yao, Yiduo, Wang, Xiao, Hao, Xudong, Sun, Hongyu, Dong, Ruixin, Li, Yansheng
Format:	Journal Article
Language:	English
Published:	Switzerland MDPI AG 26.09.2025
Subjects:	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 Access:	Get full text
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