Instead of a priori defining ERP components of interest, we use a purely data-driven approach to identify temporally specific, maximally discriminative task-relevant projections of the EEG data. Specifically, our multivariate discrimination improves identification of task-relevant components in low signal-to-noise ratio environments, such as EEG recorded during MRI acquisition. It also enables us to study the BOLD correlates of continuously-evolving components linked to the task (i.e. defined by trial labels). Since we analyze the BOLD signal by first regressing out the variance linked to observed stimulus and behavioral events (Feige et al., 2005; Goldman et al., 2009), these methods also allow us to investigate the BOLD correlates of modulations in task-engagement that are undetectable with traditional methods, and dissociate them from observable behavioral variability. Furthermore, we investigate the correlates of these modulations only for target trials (i.e. identical stimuli), thus ensuring that the trial-to-trial variation in neural processes is reflecting a latent state. Despite making no prior assumptions about functional connectivity between brain regions and without aiming to study functional networks, we find that for this simple target detection task, regions
