Title: ArtifactGen: Benchmarking WGAN-GP vs Diffusion for Label-Aware EEG Artifact Synthesis
Primary area: applications to neuroscience & cognitive science
Keywords: EEG, Generative Models, Diffusion Models, GANs, Time-Series, Neurophysiology, Data Augmentation, Artifact Synthesis, Healthcare AI, Benchmarking

Abstract: Artifacts in electroencephalography (EEG)---muscle, eye movement, electrode, chewing, and shiver---confound automated analysis yet are costly to label at scale. We study whether modern generative models can synthesize realistic, label-aware artifact segments suitable for augmentation and stress-testing. Using the TUH EEG Artifact (TUAR) corpus, we curate subject-wise splits and fixed-length multi-channel windows (e.g., 250 samples) with preprocessing tailored to each model (per-window min--max for adversarial training; per-recording/channel $z$-score for diffusion). We compare a conditional WGAN-GP with a projection discriminator to a 1D denoising diffusion model with classifier-free guidance, and evaluate along three axes: (i) fidelity via Welch band-power deltas ($\Delta\delta,\ \Delta\theta,\ \Delta\alpha,\ \Delta\beta$), channel-covariance Frobenius distance, autocorrelation $L_2$, and distributional metrics (MMD/PRD); (ii) specificity via class-conditional recovery with lightweight $k$NN/classifiers; and (iii) utility via augmentation effects on artifact recognition. In our setting, WGAN-GP achieves closer spectral alignment and lower MMD to real data, while both models exhibit weak class-conditional recovery, limiting immediate augmentation gains and revealing opportunities for stronger conditioning and coverage. We release a reproducible pipeline---data manifests, training configurations, and evaluation scripts---to establish a baseline for EEG artifact synthesis and to surface actionable failure modes for future work.

TL;DR: On TUAR, we benchmark WGAN-GP vs diffusion for label-aware EEG artifact synthesis; WGAN leads on spectral/MMD, but both struggle with class recovery; reproducible eval pipeline.