This article presents a new, more comprehensive view of event-related brain dynamics based on an information-based approach to modeling electroencephalographic (EEG) dynamics. Traditional EEG research often focuses on either peaks in average event-related potentials (ERPs) or changes in the EEG power spectrum induced by experimental events. However, these measures do not fully capture the event-related dynamics in the data and cannot isolate signals from different cortical areas. The authors propose that many ERP and other EEG features are better viewed as time/frequency perturbations of underlying field potential processes. The new approach combines independent component analysis (ICA), time/frequency analysis, and trial-by-trial visualization to measure EEG source dynamics without requiring an explicit head model. EEG signals are produced by partial synchronization of neuronal-scale field potentials across areas of the cortex. This synchronization may optimize communication between spike-mediated 'top-down' and 'bottom-up' processes, particularly during the anticipation and attention to meaningful events. The new approach requires a new data analysis method that combines signal processing and visualization to better model the spatially distributed event-related EEG dynamics supporting cognitive events. Traditional analysis of event-related EEG data proceeds in two directions: time-domain and frequency-domain. The time-domain approach averages data trials to produce an ERP waveform, while the frequency-domain approach averages changes in the frequency power spectrum to produce an event-related spectral perturbation (ERSP). Neither ERP nor ERSP fully models the dynamics of event-related data. ERP averaging filters out most of the EEG data, leaving only a small portion phase-locked to the events. ERP and ERSP are nearly complementary but do not fully capture the dynamics. The authors propose a new approach that uses ICA, time/frequency analysis, and trial-by-trial visualization to model event-related brain dynamics. ICA can separate EEG data into functionally distinct sources, and time/frequency analysis can characterize event-related perturbations in the oscillatory dynamics of ongoing EEG signals. The new approach allows for a more accurate model of EEG dynamics and may help to bring non-invasive and invasive electrophysiological research into more direct relationship. An open-source toolbox, EEGLAB, is available for this analysis. The authors also discuss potential pitfalls and future research directions, including the need to better characterize trial-to-trial and subject-to-subject variability in EEG dynamics.This article presents a new, more comprehensive view of event-related brain dynamics based on an information-based approach to modeling electroencephalographic (EEG) dynamics. Traditional EEG research often focuses on either peaks in average event-related potentials (ERPs) or changes in the EEG power spectrum induced by experimental events. However, these measures do not fully capture the event-related dynamics in the data and cannot isolate signals from different cortical areas. The authors propose that many ERP and other EEG features are better viewed as time/frequency perturbations of underlying field potential processes. The new approach combines independent component analysis (ICA), time/frequency analysis, and trial-by-trial visualization to measure EEG source dynamics without requiring an explicit head model. EEG signals are produced by partial synchronization of neuronal-scale field potentials across areas of the cortex. This synchronization may optimize communication between spike-mediated 'top-down' and 'bottom-up' processes, particularly during the anticipation and attention to meaningful events. The new approach requires a new data analysis method that combines signal processing and visualization to better model the spatially distributed event-related EEG dynamics supporting cognitive events. Traditional analysis of event-related EEG data proceeds in two directions: time-domain and frequency-domain. The time-domain approach averages data trials to produce an ERP waveform, while the frequency-domain approach averages changes in the frequency power spectrum to produce an event-related spectral perturbation (ERSP). Neither ERP nor ERSP fully models the dynamics of event-related data. ERP averaging filters out most of the EEG data, leaving only a small portion phase-locked to the events. ERP and ERSP are nearly complementary but do not fully capture the dynamics. The authors propose a new approach that uses ICA, time/frequency analysis, and trial-by-trial visualization to model event-related brain dynamics. ICA can separate EEG data into functionally distinct sources, and time/frequency analysis can characterize event-related perturbations in the oscillatory dynamics of ongoing EEG signals. The new approach allows for a more accurate model of EEG dynamics and may help to bring non-invasive and invasive electrophysiological research into more direct relationship. An open-source toolbox, EEGLAB, is available for this analysis. The authors also discuss potential pitfalls and future research directions, including the need to better characterize trial-to-trial and subject-to-subject variability in EEG dynamics.