Measuring Mental Workload Using ERPs Based on FIR, ICA, and MARA

Mental workload is considered to be strongly linked human performance, and the ability measure it accurately key for balancing health work. In this study, brain signals were elicited by mental arithmetic tasks of varying difficulty stimulate different levels workload. addition, a finite impulse response (FIR) filter, independent component analysis (ICA), multiple artifact rejection algorithms (MARAs) used filter event-related potentials (ERPs). Then, data consisting ERPs, subjective ratings workload, task analyzed through use variance Spearman’s correlation during simulated computer task. We found that participants responded faster performed better in easy condition, followed medium high-difficulty conditions, which verifies validity ERP filtering. Moreover, larger P2 P3 waveforms evoked as increased, higher more enhanced N300. Correlation revealed negative relationship between amplitude ratings, positive accuracy. The results presented paper demonstrate combination FIR, ICA, MARA methods can ERPs non-invasive real-time measurement Additionally, frontocentral P2, N3, parietal components showed differences genders. proposed useful identification states applied human–computer interaction future.