Eeg-Based Mental Workload Estimation

Knowledge of the level of mental workload induced by any task is essential for optimizing load share among the operators. This helps in assessing their capability; besides, helping in task allocation. Since a persistently high workload experienced by operators such as aircraft pilots and automobile drivers many times compromises their performance and safety. Despite the availability of various mental workload evaluation techniques such as heart rate variability, pupil dilation, sac-cades, etc., assessment of mental workload is still a challenging task. In this work, we aim to evaluate the workload of the operator involved in long duration tasks. For this, experiments have been carried out in a working environment which provides tasks to be done simultaneously, tasks with a pause or break in activity and cross-functional tasks. The experiment data is recorded continuously in different modes and analyzed in segments to show the change in mental workload. The artificial neural network (ANN) architecture classified the workload data with an accuracy of 96.6%. The brain connectivity analysis shows the efficacy of the proposed approach.