Modelling cognitive states from EEG data is a crucial area in neuroscience and human-computer interaction, particularly for understanding user behaviour in an e-learning environment.

The goal of this research is to develop a novel methodology that uses deep clustering of EEG microstates to identify and model different cognitive states, moving beyond traditional performance metrics to provide richer insights into user experiences and task performance.