Fig. 2

Proposed machine learning pipeline: we collect EEG and multimodal physiological data from suitable sensors. To clean the data for further processing, we used individual phases of feature selection, feature prepossessing and feature constructions for model selection which was used for parameter optimisation. This process was repeated using automated ML for the best possible outcome from the collected data set. After model validation, we use our trained model for meltdown moment detection, workplace stress detection, VRET and/or other domains where arousal detection is crucial