Unsupervised clustering of pre-ictal EEG in children: a reproducible and lightweight CPU-based workflow

The unsupervised nature of the study approach provides experimental evidence for the demarcation of a number of distinct pre-ictal states. These are associated with changes in cortical excitability and network synchrony, consistent with the predicted dynamics of a model of epilepsy. This study should be regarded as a proof-of-concept that advances methodological aspects of unsupervised EEG clustering within this specific dataset. The findings are hypothesis-generating rather than conclusive, providing a preliminary platform for exploring automatic pre-ictal state monitoring without expert input.