Most patients who experience cardiac arrest remain comatose after restoration of blood circulation due to post-anoxic brain injury. Accurate outcome prediction is essential for guiding clinical management and communicating with relatives. Among the various neuroprognostication modalities, resting-state electroencephalogram (EEG) analysis is the most widely used, as it directly reflects brain activity. Currently, EEG-based clinical evaluation relies on visual interpretation, which is prone to intra- and inter-rater variability. To overcome these limitations, computer-based methods, particularly convolutional neural networks (CNNs), have recently emerged as promising alternatives. The aim of this work is to investigate the potential of a CNN to predict post-cardiac arrest outcome from EEG data, based on Cerebral Performance Category (CPC).