Two major non-invasive techniques in cognitive neuroscience, electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), have complementary advantages with regard to their spatial and temporal resolution. Recent hardware and software developments have made it feasible to acquire EEG and fMRI data simultaneously. We emphasize the potential of simultaneous EEG and fMRI recordings to pursue new strategies in cognitive neuroimaging. Specifically, we propose that, by exploiting the combined spatiotemporal resolution of the methods, the integration of EEG and fMRI recordings on a single-trial level enables the rich temporal dynamics of information processing to be characterized within spatially well-defined neural networks.Introduction Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) recordings provide complementary advantages with regard to the temporal and spatial resolution of brain activity. fMRI enables brain regions engaged during cognitive processes to be localized with high spatial precision; however, the blood-oxygenationlevel-dependent (BOLD) response is too slow to capture fully the rich temporal dynamics that underlie cognitive processes. By contrast, the EEG signal is directly coupled to neuronal electrical activity and has millisecond precision; however, EEG picks up a blurred spatial mixture of the underlying cortical activity and therefore provides only limited spatial resolution. Accordingly, great hope lies in the integration of EEG and fMRI to achieve both high temporal and high spatial resolution of human brain function [1][2][3][4][5]. Here, we propose that simultaneous EEG and fMRI recordings provide a major improvement that will advance considerably our understanding of how cognitive functions are implemented by the brain. Importantly, simultaneous EEG-fMRI recordings enable the investigation of trial-by-trial fluctuations of brain activity, which reveals important insights into the dynamics of cognitive function.The fundamental assumption behind any integration approach is that the signals recorded in both modalities are at least partly produced by the same neural generators.