This presentation will provide an overview of our group’s work using electrophysiological measures (i.e. electroencephalography (EEG) and event-related potentials (ERP)) of brain activity to study individuals who have sustained a brain injury. In order to examine the consequences of brain injury, we have taken advantage of the component structure of ERPs in which particular time-based features of brain responses to stimulus environments reflect functions such as attention, memory, and language comprehension. Work will be described demonstrating the value of this approach in assessing functional capacity in a variety of patients including those who have been diagnosed as being in a vegetative state, in a coma, or having sustained a concussion. The more traditional approaches to signal processing will be discussed followed by a presentation of the significant “value-added” in using machine learning (ML) methods to provide more fine-grained assessment of the neurophysiological signals obtained in these different patient populations. The talk will discuss a number of different approaches that were taken to apply ML to these types of biological signals, challenges that particularly affect these applications (e.g., limited sample sizes, skewed classes, and varying SNRs), and strategies our group has taken to bridge that gap in the domain of clinical EEG/ERP.