Estimation of task workload from EEG data: New and current tools and perspectives

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 Bat...

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Bibliographic Details
Published in2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society Vol. 2011; pp. 6547 - 6551
Main Authors Kothe, C. A., Makeig, S.
Format Conference Proceeding Journal Article
LanguageEnglish
Published United States IEEE 01.01.2011
Subjects
Algorithms
Artificial Intelligence
Brain models
Calibration
Cognition
Computer Simulation
Electroencephalography
Electroencephalography - methods
Estimation
Feature extraction
Humans
Learning systems
Monitoring
Neural Networks (Computer)
Neurosciences
Oscillometry
Regression Analysis
Reproducibility of Results
Signal Processing, Computer-Assisted
Software
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Summary: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.
ISBN:9781424441211
1424441218
ISSN:1094-687X
1557-170X
DOI:10.1109/IEMBS.2011.6091615