Probing neural activations from continuous EEG in a real-world task: time-frequency independent component analysis.

It is of fundamental significance to study human brain functions using neuroimaging technologies, such as electroencephalograph (EEG) and functional magnetic resonance imaging (fMRI), in real-world tasks. The present study explores the feasibility of using EEG to identify networked brain activations when subjects perform a realistic task. To robustly identify physiologically plausible EEG patterns related to brain activations involved in the task, a novel data-driven method, i.e., time-frequency independent component analysis (tfICA), is developed to analyze high-density EEG data, which combines the time-frequency analysis and complex-valued ICA method. Six classes of independent components (ICs) of various spatio-temporal-spectral patterns were identified across subjects, relating to frontal, motor, premotor, supplementary motor, secondary somatosensory, and occipital cortices, which suggest a networked brain activation involving visual perception and processing, movement planning and execution, working memory, performance monitoring, and decision making to accomplish the task. Our results indicate that temporal patterns of these ICs are consistent, show causal relationship among them, and of significant correlation to behavioral performance data recorded in same task sessions. Furthermore, the time-on-task effect that indicates the phenomenon of mental fatigue in sustained tasks for a long duration (i.e., 1h) was observed. The present study demonstrates the capability of the tfICA method in distinguishing various brain processes from continuous EEG data obtained in a realistic task and it is thus promising to address real-world problems, such as time-on-task fatigue.