  This work investigates the predictive potential of bipolar
electroencephalogram (EEG) recordings towards efficient prediction of poor
neurological outcomes. A retrospective design using a hybrid deep learning
approach is utilized to optimize an objective function aiming for high
specificity, i.e., true positive rate (TPR) with reduced false positives (<
0.05). A multi-channel EEG array of 18 bipolar channel pairs from a randomly
selected 5-minute segment in an hour is kept. In order to determine the outcome
prediction, a combination of a feature encoder with 1-D convolutional layers,
learnable position encoding, a context network with attention mechanisms, and
finally, a regressor and classifier blocks are used. The feature encoder
extricates local temporal and spatial features, while the following position
encoding and attention mechanisms attempt to capture global temporal
dependencies. Results: The proposed framework by our team, OUS IVS, when
validated on the challenge hidden validation data, exhibited a score of 0.57.
