The development of new treatments for neuropsychiatric disorders requires the development of physiological measures that can accurately translate between preclinical animal models and clinical human studies. Neurophysiological measures, especially event-related potentials (ERP), provide effective physiological read-outs of the flow of information from primary sensory through higher-order associative brain regions and thus can be used to investigate mechanisms underlying cognitive impairments across neuropsychiatric disorders. Traditional "time-domain" event-related potentials (ERP) such as auditory P300 and mismatch negativity or visual P1 and face N170 are increasingly being used in clinical studies for patient stratification, outcome prediction, or target engagement. Nevertheless, time-domain approaches use only a small portion of the information inherent within the event-related EEG signal. Newer, time-frequency (TF-ERP) approaches provide additional information along with improved translational utility and may be especially useful in differentiating activity related to thalamocortical driver versus modulatory inputs, as well as detecting event-related modulations of ongoing EEG power. The utility of the TF-ERP approach may be further enhanced by using source-space analytic approaches, including newer Beamformer approaches which are sensitive to both power within identified brain regions and coherence between brain regions. In addition to supporting the development of novel pharmacological agents, such methods may be guiding personalized, high-definition neuro-modulatory intervention approaches.