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arXiv cs.LG
arXiv cs.LG
5/13/2026
Interpretable EEG Microstate Discovery via Variational Deep Embedding: A Systematic Architecture Search with Multi-Quadrant Evaluation

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

Short summary

Researchers present Conv-VaDE, a deep learning model for interpretable EEG microstate analysis that replaces hard clustering with probabilistic soft assignment. Systematic architecture search reveals that moderately deep networks (L=4) with compact channels consistently achieve superior performance (GEV=0.730, silhouette=0.229). Results show principled architecture design, not merely model scale, is key to stable and interpretable brain state discovery.

  • •Conv-VaDE enables interpretable EEG analysis via probabilistic soft clustering and generative decoding of topographic patterns
  • •Architecture search found moderately deep networks (L=4) with compact channels dominate across all configurations
  • •Principled architecture search outweighs model scale for achieving interpretable microstate discovery

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