Objective measurement methods to quantify the health status of individuals date back a long time. As one such example, electroencephalography (EEG) has been used for over a century to analyze electric currents in and around the brain and is used widely in clinical healthcare systems. Unfortunately, gold-standard clinical EEG systems usually require a lot of time to set up, are limited in mobility, and are not designed to look inconspicuous. However, as EEG directly measures electrical correlates of the brain with a high temporal resolution, it is an important measurement method and of high interest for mental state classification. Therefore, wearable EEG devices are being developed for various use cases.
One area of application of wearable EEG devices that holds great promise is the classification of mental workload and stress. “Mental workload may be viewed as the difference between the capacities of the information processing system that are required for task performance to satisfy performance expectations and the capacity available at any given time.” (from: ). Additionally, it has been shown that high levels of mental workload over long periods of time can lead to an increased risk of coronary heart disease and hypertension, amongst others. Mental workload and stress are closely related topics, and a high mental workload is regarded as a preliminary factor for stress. When mental workload increases, stress levels rise and mental fatigue can be developed, which might result in deteriorating mental health. While not every mental workload directly results in work-related stress, the long-term consequences of high levels of mental workload and stress might be very costly, which is why accurate time-series classification (TSC) is so important.
We want to move this technology directly where it is needed: outside of residing only in clinical or laboratory environments, bringing it right into the hands of everyone, ready to use and to assist in living a longer and healthier life! For this purpose, we conducted a systematic literature review on mental state classification using wearable EEG. Alongside many findings on data pre-processing, the applicability of artificial intelligence and more, one of our findings was that multi-modal TSC applications outperform their best unimodal counterparts. Consequently, in our ongoing studies, we are combining the utilization of wearable EEG with other wearable devices capable of measuring additional physiological signals, such as the heart rate.
Our current and future work is focusing on differentiating levels of mental workload and mental workload task types in controlled, semi-controlled, and uncontrolled environments. These studies aim at assessing the influence of noise on recordings of psychophysiological correlates of mental workload, investigating the reliability of wearable sensor systems on mental workload classification, and on creating publicly available data sets to enable national and international collaboration on these important topics. Furthermore, we want to work on automated techniques for the identification and removal of artefacts, are planning on investigating using Bio-/Neurofeedback as an intervention and as a means for noise-reduction, and are working towards conducting basic research on the potential possibility of self-modulation of response-strength to a given stimulus.
For our current studies revolving around the field of 'Measuring and Quantifying Mental Workload and Stress in Everyday Situations', we are constantly searching for interested study participants. Interested individuals wanting to participate in one of our studies and learn more about the study procedures, how to assist, remuneration for participation, and to find additional information are highly encouraged to reach out to me. Furthermore, master's students interested in writing a master's thesis on, or with aspects of, the analysis of multi-modal time-series data in healthcare, please reach out to me and let us discuss possible topics. I am interested in supervising various projects if they evolve around classification, prediction, or forecasting tasks on time-series data obtained from wearable on-body sensor systems, especially investigating the effects of data pre-processing, with a solid contribution of EEG.