A proposal for improving EEG microstate generation via interpretable deep clustering with convolutional autoencoders

Electroencephalography-based microstates, characterised as quasi-stable states of mental activation, encapsulate the spatio-temporal dynamics of brain signals. They are representative template topographic maps for time intervals, usually in the order of 60-120ms, extracted from the whole time duration of an EEG-based experiment. This extraction is currently performed with shallow clustering algorithms such as k-means or hierarchical clustering and trained with many 1D vectors containing scalp-electrode activations, each representing an instance point in time. However, this approach ignores the spatial position of these electrodes, which we argue is essential information for improving cluster formation. This study contributes to the body of knowledge by introducing deep clustering, leveraging recent advancements in computer vision, and autonomously learning feature representations from EEG data during clustering, capturing the inherent structure and patterns more effectively. In addition, relevant advances in eXplainable Artificial Intelligence have enabled various attribution methods to interpret trained models, which can be exploited to understand the inherent mechanisms for microstate formation.