Quantifying Mental Workload and Stress in Everyday Situations
Mental workload is a term from cognitive psychology which refers to the amount of working memory used in the brain. The ratio of the occupied processing capability of the working memory and the amount required by the task can be referred to as mental workload. Therefore, identifying a potential mental overload is essential, especially for drivers, pilots, medical professionals, emergency workers, and air traffic control professionals. Moreover, complex cognitive tasks alone or combined with other factors like time or social pressure can release cortisol resulting in psychological stress, a primary premonitory symptom of an epileptic seizure. Therefore, this project aims to predict the mental workload to provide pre-emptive therapy for epilepsy patients.
This project was started by curating EEG data from 11 healthy participants while performing mental workload and relaxation tasks in a controlled environment. The recent development of wearable EEG devices made data collection convenient through fewer electrodes. Frequency domain features were extracted from raw EEG data after pre-processing using outlier rejection based on a movement filter, spectral filtering, common average referencing, and normalization. An exploratory feature analysis shows that brain asymmetry features are the most important to predict mental workload. After that, the data collection is extended to include multimodality i.e., PPG and EDA sensors and daily life data collection along with the controlled environment data. The data collection and recruitment is ongoing with a successful data collection from 20 participants. The research question of how reliably mental workload can be predicted mainly in daily life with providing information on the important modalities is yet to be answered from this project.