Description

In particular, the development of operator mental state monitoring utilizing electroencephalogram (EEG) inputs is made possible by machine learning and its subsection deep learning approaches. Although several machine learning techniques have recently been presented for identifying cognitive stress using EEG. There does not yet exist a review that covers in-depth the application of machine learning methods. To alleviate this gap, in this paper, we survey cognitive workload and machine learning literature to identify the approaches and highlight the primary advances. This project is based on finding the workload classification as normal, medium, high stress level by the prediction of temperature, humidity and step count and also calculate the comparison fo the three ML algorithms