Mental workload level assessment based on compounded hysteresis effect.

In the domain of neuroergonomics, cognitive workload estimation has taken a significant concern among the researchers. This is because the knowledge gathered from its estimation is useful for distributing tasks among the operators, understanding human capability and intervening operators at times of havoc. Brain signals give a promising prospective for understanding cognitive workload. For this, electroencephalography (EEG) is by far the most efficient modality in interpreting the covert information arising in the brain. The present work explores the feasibility of EEG rhythms for monitoring continuous change occurring in a person's cognitive workload. This continuous monitoring is achieved by graphicallyinterpreting the cumulative effect of changes in EEG rhythms observed in the current instance and the former instance based on the hysteresis effect. In this work, classification is done to predict the data class label using an artificial neural network (ANN) architecture. The proposed model gives a classification accuracy of 98.66%.

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.

Correlation of P300 ERPs with visual stimuli and its application to vigilance detection.

Vigilance or sustained attention is defined as the ability to maintain concentrated attention over prolonged time periods. It is an important aspect in industries such as aerospace and nuclear power, which involve tremendous man-machine interaction and where safety of any component/system or environment as a whole is extremely crucial. Many methods for vigilance detection, based on biological and behavioral characteristics, have been proposed in the literature. Nevertheless, the existing methods are associated with high time complexity, unhandy devices and incur huge equipment overhead. This paper aims to pave an alternative solution to the existing techniques using brain computing interface (BCI). EEG device being a non-invasive BCI technique is popular in many applications. In this work, we have utilized P300 component of ERPs of EEG signal for vigilance detection task as it can be detected fast and accurately. Through this work, we aim to establish the correlation between P300 ERP and vigilance. We have performed a number of experiments to substantiate the correctness of our proposal and have also proposed an approach to measure the vigilance level.