title: Estimation of task workload from EEG data: new and current tools and perspectives.
abstract: We report, as part of the EMBC meeting Cognitive State Assessment (CSA) competition 2011, an empirical comparison using robust cross-validation of the performance of eleven computational approaches to real-time electroencephalography (EEG) based mental workload monitoring on Multi-Attribute Task Battery data from eight subjects. We propose a new approach, Overcomplete Spectral Regression, that combines several potentially advantageous attributes and empirically demonstrate its superior performance on these data compared to the ten other CSA methods tested. We discuss results from computational, neuroscience and experimentation points of view.
authors.affiliation: Swartz Center for Computational Neuroscience, Institute for Neural Computation, UCSD, La Jolla, CA 92093-0559, USA. christian@sccn.ucsd.edu
authors.forename: Christian A
authors.initials: CA
authors.lastname: Kothe
authors.forename: Scott
authors.initials: S
authors.lastname: Makeig
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D002138:Calibration; D003071:Cognition; D003198:Computer Simulation; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D016571:Neural Networks, Computer; D009488:Neurosciences; D009991:Oscillometry; D012044:Regression Analysis; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D012984:Software