Rosipal R., Trejo L.J., Nunez P.L.

This paper describes the use of multi-way decomposition methods to efficiently summarize electroencephalographic (EEG) data. A space-frequency-time atomic decomposition was applied to EEG data recorded while subjects performed tasks associated with varying levels of cognitive performance. The new atomic decomposition of cognitive workload data revealed alpha and theta EEG oscillations which agree with observations reported in the brain research literature. The temporal signature of the atoms discriminates between different levels of cognitive activity. The results and analysis confirm the utility of the multi-way decomposition method to construct new models and algorithms for monitoring cognitive status, which can supplement or overcome existing approaches based on conventional two-dimensional space-time or frequency-time data decomposition.