Interpretable EEG Microstate Discovery via Variational Deep Embedding: A Systematic Architecture Search with Multi-Quadrant Evaluation

Authors: Saheed Faremi, Andrea Visentin, Luca Longo

arXiv · PDF

Summary

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.

Relevance

Not related to my LLM persona work — this is about discovering discrete brain states in EEG data. The methodological parallel (soft clustering in latent space, generative decoding of cluster prototypes) is conceptually interesting but not directly applicable to behavioral installation in language models.

Abstract

arXiv:2605.10947v1 Announce Type: new Abstract: EEG microstate analysis segments continuous brain electrical activity into brief, quasi-stable topographic configurations that reflect discrete functional brain states. Conventional approaches such as Modified K-Means operate directly in electrode space with hard assignment, offering no learned latent representation, no generative decoder, and no mechanism to decode latent configurations into verifiable scalp topographies, limiting both model transparency and interpretability. To address this, we present a Convolutional Variational Deep Embedding (Conv-VaDE) model that jointly learns topographic reconstruction and probabilistic soft clustering in a shared latent space. Conv-VaDE enables generative decoding of cluster prototypes into verifiable scalp topographies, replacing opaque hard partitioning with probabilistic soft assignment. A polarity invariance scheme and a four-dimensional grid search over cluster count (K from 3 to 20), latent dimensionality, network depth, and channel width are conducted to systematically reveal how each architectural design choice shapes the quality, stability, and interpretability of learned EEG microstate representations. The model is evaluated on the LEMON resting-state eyes-closed EEG dataset with ten participants using topographic template formation, clustering stability, and global explained variance (GEV). The architecture search reveals that depth L = 4 appears consistently across all 18 best-performing configurations, yielding a best-case GEV of 0.730 and a silhouette of 0.229 at K = 4 across the model sweeps, where moderately deep networks with compact channel widths and small latent dimensionality dominate across the full K range. These results establish that principled architecture search, rather than model scale, is the key to interpretable and stable EEG microstate discovery via variational deep embedding.