It is difficult to determine future patient outcomes once they have suffered a traumatic brain injury. Factors such as the time of injury, location, severity, and treatment response all have a role in predicting the patients future outcomes, however, current models struggle to determine the prediction of outcomes for patients. The research in this study was performed by detecting changes in brain activity related to traumatic brain injuries through electroencephalography (EEG) using studies from different databases and analyzing them using QUIPS (QUality in Prognostic Studies) and PROBAST (Prediction model Risk Of Bias ASsessment Tool). There were 27 manuscripts that met the inclusion criteria of the study, however, all contained moderate amounts of bias. The EEG data was studied through feature detection and was found to be a significant predictor of patient outcomes up to a year and a half after they experienced their traumatic brain injury. Better patient outcomes surrounded more continuous EEG activity, EEG reactivity, and specific EEG sleep features. A model containing these parameters and in combination with clinical parameters was found to be accurate in prognostication after a traumatic brain injury. Although these results were found in this study, the authors concluded that more research surrounding EEG features and traumatic brain injury prognostication is needed to show valuable insights and further explanations.