Generative mechanisms and scaling laws of EEG suggest an alternative physiological interpretation of ICA

In this study, we subject the conventional physiological interpretation of independent component analysis (ICA) applied to EEG, the small-patch model, to systematic falsification, and propose an alternative large-patch model. The small-patch model assumes that ICs correspond to localized cortical patches with < 1 cm{superscript 2}. However, this assumption has remained unvalidated. The small-patch model predicts that approximately 70% of sources are localized within sulci up to 15 mm deep, with rapidly changing dipole orientations across the cortex. In contrast, the large-patch model (>6-10 cm{superscript 2}) predicts relatively stable radial orientations accompanied by physiologically implausible source depths due to depth bias. First, we conducted a stimulation study using a forward-inverse modeling framework with a four-layer head conductor model. We confirmed that depth bias emerges when a single equivalent dipole is fitted to a potential field generated by a broad array of parallel dipoles. This observation led to the key hypothesis that the presence of depth bias in empirical data would favor the large-patch model. Second, we analyzed resting-state EEG from two European open datasets comprising 820 recordings (62-64 channels), yielding dipole depth and orientation distributions for nearly 15,000 qualified brain ICs. Results showed that more than 80% of ICs were localized at physiologically implausible depths (19-26 mm), favoring the large-patch model. A novel dipole-orientation analysis revealed broad, low-spatial-frequency structure in dipole orientations, further supporting the large-patch model. We conclude that the revised physiological interpretation of ICA aligns with electrophysiological literature and computational insights into EEG-specific spatial scaling laws. Significance statementIndependent component analysis (ICA) has been proposed as a promising tool for computational neuroscience using human scalp EEG. One of the original proponents introduced a physiological model suggesting that anatomically accurate neural sources could be directly recovered by applying ICA to EEG data. However, we found that this model assumes EEG generation within cortical patches smaller than 1 cm{superscript 2}, which has remained unvalidated for over a decade and requires revision. Using both simulation and empirical EEG datasets, we demonstrated that our alternative model, involving larger cortical patches (>6-10 cm{superscript 2}), better fits the electrophysiological generative model of scalp EEG signals. We conclude that our large-patch model provides an updated, more physiologically plausible interpretation of ICA results.