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

This paper introduces the Convolutional Variational Deep Embedding (Conv-VaDE) model for EEG microstate analysis. It enhances interpretability by jointly learning topographic reconstruction and probabilistic soft clustering, enabling generative decoding of cluster prototypes into verifiable scalp topographies.