The authors develop a variational deep embedding model (Conv-VaDE) for discovering "microstates" in EEG brain recordings — brief, stable patterns of electrical activity that represent discrete functional brain states. Traditional methods use hard clustering directly on electrode measurements, with no learned representation or way to decode what each cluster looks like. Conv-VaDE learns a shared latent space that does both reconstruction and probabilistic soft clustering, allowing the model to generate verifiable scalp topographies for each cluster. They run a systematic architecture search over cluster count, latent dimensionality, network depth, and channel width on resting-state EEG data.
Main takeaways:
- Conventional EEG microstate analysis uses hard clustering in electrode space with no learned latent representation or generative decoding.
- Conv-VaDE jointly learns topographic reconstruction and probabilistic soft clustering in a shared latent space, enabling generative decoding of cluster prototypes.
- A four-dimensional architecture search reveals that depth L=4 appears in all 18 best-performing configurations.
- Best results achieve 73% global explained variance and 0.229 silhouette score at K=4 clusters.
- Moderately deep networks with compact channel widths and small latent dimensionality dominate across the full cluster-count range.