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.
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