This article presents a novel approach to modeling event-related brain dynamics using an information-based framework for electroencephalographic (EEG) data. Traditional methods, such as event-related potentials (ERPs) and event-related spectral perturbations (ERSPs), focus on either peak responses or changes in EEG power spectra, respectively, but these approaches do not fully capture the complex dynamics of brain activity. The authors propose that ERPs and other EEG features can be better understood as time/frequency perturbations of underlying field potential processes. They introduce a combination of independent component analysis (ICA), time/frequency analysis, and trial-by-trial visualization to model EEG source dynamics without requiring explicit head models. This approach allows for a more comprehensive understanding of the spatially distributed event-related EEG dynamics that support cognitive events. The article also discusses the limitations of traditional methods, such as signal mixing and phase resetting, and highlights the benefits of the new approach, including improved physiological plausibility and better linkages to behavior. The authors conclude by suggesting that this novel method may help bridge the gap between non-invasive and invasive electrophysiological research.This article presents a novel approach to modeling event-related brain dynamics using an information-based framework for electroencephalographic (EEG) data. Traditional methods, such as event-related potentials (ERPs) and event-related spectral perturbations (ERSPs), focus on either peak responses or changes in EEG power spectra, respectively, but these approaches do not fully capture the complex dynamics of brain activity. The authors propose that ERPs and other EEG features can be better understood as time/frequency perturbations of underlying field potential processes. They introduce a combination of independent component analysis (ICA), time/frequency analysis, and trial-by-trial visualization to model EEG source dynamics without requiring explicit head models. This approach allows for a more comprehensive understanding of the spatially distributed event-related EEG dynamics that support cognitive events. The article also discusses the limitations of traditional methods, such as signal mixing and phase resetting, and highlights the benefits of the new approach, including improved physiological plausibility and better linkages to behavior. The authors conclude by suggesting that this novel method may help bridge the gap between non-invasive and invasive electrophysiological research.