This thesis investigates the potential of EEG indicators, with a main focus on brain connectivity indicators, in predicting coma outcomes, particularly in patients with traumatic brain injury (TBI). EEG data from thirteen patients were analyzed, focusing on the extraction of features related to brain connectivity and their classification using various machine learning models. The methodology involved three approaches: a non-standard-function-based method and two standard-function-based methods, tailored and one-size-fits-all. Key indicators such as Partial Directed Coherence, Direct Transfer Function, and Granger Causality were extracted and analyzed. Results showed that the k-Nearest Neighbors (k-NN) classifier yielded the best performance, particularly when using the tailored standard-function-based approach.