Mental Workload Classification during simulated flight operations based on cardiac and neural dynamics recorded using the MUSE 2 low-cost system

Abstract

The advancement of low-cost and highly portable physiological systems presents promising opportunities for monitoring human cognitive processes during daily-life activities and more complex tasks such as operating an aircraft. The Muse 2 system combines electroencephalography (EEG) and photoplethysmography (PPG) sensors allowing the extraction of neural dynamics features in the time and frequency domains and heart rate. In a study, we equipped five pilots with the Muse 2 system while they performed a low-load and high-load traffic pattern task along with a passive auditory oddball task. The group-level analyses revealed that participants exhibited higher average heart rate, lower power spectrum density in the alpha band, decreased P300 amplitude in the high-load compared to the low-load condition. These results are in line with previous laboratory research conducted in highly controlled settings and research-grade instrumentations. The classification of the two levels of mental workload reached 93.2% accuracy on a single-trial basis based on EEG frequency features. Post-hoc analysis revealed that the classifier mainly relied on motion artefact features in the beta and gamma bands. The classifiers using heart rate and ERPs features reached 76% and 77.8% classification accuracy, respectively. Despite its interest, this system presents some limitations for mobile and neuroergonomics applications notably with regards to the limited number of electrodes preventing the use of advanced signal processing techniques to address noise and artifacts in the signals.

Keywords: Mental workload, low, cost EEG system, Muse 2, Classification, Neuroergonomics

DOI: 10.54941/ahfe1003017