title: Estimating cognitive workload using wavelet entropy-based features during an arithmetic task.
abstract: Electroencephalography (EEG) has shown promise as an indicator of cognitive workload; however, precise workload estimation is an ongoing research challenge. In this investigation, seven levels of workload were induced using an arithmetic task, and the entropy of wavelet coefficients extracted from EEG signals is shown to distinguish all seven levels. For a subject-independent multi-channel classification scheme, the entropy features achieved high accuracy, up to 98% for channels from the frontal lobes, in the delta frequency band. This suggests that a smaller number of EEG channels in only one frequency band can be deployed for an effective EEG-based workload classification system. Together with analysis based on phase locking between channels, these results consistently suggest increased synchronization of neural responses for higher load levels.
authors.affiliation: School of Electrical Engineering and Telecommunications, The University of New South Wales, Sydney, NSW 2052, Australia; ATP Research Laboratory, National ICT Australia, Eveleigh, NSW 2015, Australia. Electronic address: p.zarjam@student.unsw.edu.au.
authors.forename: Pega
authors.initials: P
authors.lastname: Zarjam
authors.forename: Julien
authors.initials: J
authors.lastname: Epps
authors.forename: Fang
authors.initials: F
authors.lastname: Chen
authors.forename: Nigel H
authors.initials: NH
authors.lastname: Lovell
mesh_terms: D000328:Adult; D004569:Electroencephalography / Q000379:methods*; D019277:Entropy; D006801:Humans; D008297:Male; D011340:Problem Solving / Q000502:physiology*; D012815:Signal Processing, Computer-Assisted
keywords: Delta band; EEG; Entropy features; Frontal lobe; Memory workload