An Interpretable Deep Learning Model for EEG Signals

Cutting-edge methods in artificial intelligence (AI) have the ability to significantly improve outcomes. However, the struggle to interpret these black box models presents a serious problem to the industry. When selecting a model, the decision to sacrifice accuracy for interpretability must be made. In this paper, we consider a case study on eye state detection using electroencephalogram (EEG) signals to investigate how a deep neural network (DNN) model makes a prediction, and how that prediction can be interpreted.