Efficient mental workload estimation using task-independent EEG features.

Abstract

Mental workload is frequently estimated by EEG-based mental state monitoring systems. Usually, these systems use spectral markers and event-related potentials (ERPs). To our knowledge, no study has directly compared their performance for mental workload assessment, nor evaluated the stability in time of these markers and of the performance of the associated mental workload estimators.  This study proposes a comparison of two processing chains, one based on the power in five frequency bands, and one based on ERPs, both including a spatial filtering step (respectively CSP and CCA), an FLDA classification and a 10-fold cross-validation. To get closer to a real life implementation, spectral markers were extracted from a short window (i.e. towards reactive systems) that did not include any motor activity and the analyzed ERPs were elicited by a task-independent probe that required a reflex-like answer (i.e. close to the ones required by dead man's vigilance devices). The data were acquired from 20 participants who performed a Sternberg memory task for 90 min (i.e. 2/6 digits to memorize) inside which a simple detection task was inserted. The results were compared both when the testing was performed at the beginning and end of the session. Both chains performed significantly better than random; however the one based on the spectral markers had a low performance (60%) and was not stable in time. Conversely, the ERP-based chain gave very high results (91%) and was stable in time. This study demonstrates that an efficient and stable in time workload estimation can be achieved using task-independent spatially filtered ERPs elicited in a minimally intrusive manner.