Abstract
EEG (electroencephalogram) recorded in human participants as they performed tasks that induced different mental states, including
engagement, mental workload, and mental fatigue. We tested two types of atomic decomposition, each of which identifies unique EEG
sources simultaneously in three dimensions: 1) atoms with dimensions of power spectral density, space (electrode position), and time (time
on task or task conditions), or 2) atoms with dimensions of magnitude squared coherence, spatial relationships (electrode pairs), and time.
For tasks that induced mental workload, we found atoms that combine sources in the theta (4-8 Hz) and alpha (8-12 Hz) EEG frequency
bands consistently in individual participants at different times of day and on different days. The temporal variations of the atoms clearly
reflected the levels of mental workload induced by varying task conditions. For a task that induced mental fatigue, we found atoms that
tracked the development of mental fatigue in individual participants over time, while reflecting underlying changes in power or coherence of
primarily theta-band EEG. Our results show that atomic decomposition is a valuable new approach to the identification and measurement of
EEG sources for monitoring cognitive status. By comparing these results with results of prior analyses using the same data sets, we observed
that atomic decomposition can supplement or overcome existing approaches based on conventional two-dimensional space-time or
frequency-time decomposition of EEG.
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