Empirical Bayesian localization of event-related time-frequency neural activity dynamics

•An empirical Bayesian framework (time-frequency Champagne, TFC) for optimal reconstruction of event-related time-frequency dynamics of regional cortical oscillations is proposed in this paper.•TFC enables imaging of five-dimensional (space, time, and frequency) event-related brain activity from M/EEG data and can be viewed as a time-frequency optimized adaptive Bayesian beamformer.•The advantages of TFC in estimating time-frequency cortical oscillatory dynamics is evaluated in both simulations and several real datasets.•TFC is able to accurately reconstruct cortical dynamics with data from just a few epochs. Accurate reconstruction of the spatio-temporal dynamics of event-related cortical oscillations across human brain regions is an important problem in functional brain imaging and human cognitive neuroscience with magnetoencephalography (MEG) and electroencephalography (EEG). The problem is challenging not only in terms of localization of complex source configurations from sensor measurements with unknown noise and interference but also for reconstruction of transient event-related time-frequency dynamics of cortical oscillations. We recently proposed a robust empirical Bayesian algorithm for simultaneous reconstruction of complex brain source activity and noise covariance, in the context of evoked and resting-state data. In this paper, we expand upon this empirical Bayesian framework for optimal reconstruction of event-related time-frequency dynamics of regional cortical oscillations, referred to as time-frequency Champagne (TFC). This framework enables imaging of five-dimensional (space, time, and frequency) event-related brain activity from M/EEG data, and can be viewed as a time-frequency optimized adaptive Bayesian beamformer. We evaluate TFC in both simulations and several real datasets, with comparisons to benchmark standards - variants of time-frequency optimized adaptive beamformers (TFBF) as well as the sLORETA algorithm. In simulations, we demonstrate several advantages in estimating time-frequency cortical oscillatory dynamics compared to benchmarks. With real MEG data, we demonstrate across many datasets that the proposed approach is robust to highly correlated brain activity and low SNR data, and is able to accurately reconstruct cortical dynamics with data from just a few epochs.