Abstract

Evaluating mental workload is crucial to preserve health and prevent mishaps. The reliability and mental states of individuals in any human-computer interaction scenario are assessed utilizing features of the electroencephalogram (EEG) by means of many approaches in machine learning and deep learning This study reviews and identifies the multiple Machine Learning and Deep Learning algorithms used for workload assessment, as well as the various datasets, characteristics, and features that contribute to workload assessment. When ML and DL approaches were compared, it was found that deep learning techniques and ensemble techniques work best when EEG's Power Spectral Density Features are used. We have also used optimization techniques like GWO and taken into account numerous features from various domains and assessed the workload. This study discovered that when measuring cognitive load, features like PSD were employed and deep learning algorithms were applied if algorithm performance was crucial. However, when accuracy was valued more highly, all features were taken into account and only a small subset of them was chosen using optimization techniques. The latter method was found to be more accurate and reliable than the methods currently in use.