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Convolutional Neural Networks (CNNs) have achieved impressive performance onmany computer vision related tasks, such as object detection, imagerecognition, image retrieval, etc. These achievements benefit from the CNNs'outstanding capability to learn discriminative features with deep layers ofneuron structures and iterative training process. This has inspired the EEGresearch community to adopt CNN in performing EEG classification tasks.However, CNNs learned features are not immediately interpretable, causing alack of understanding of the CNNs' internal working mechanism. To improve CNNinterpretability, CNN visualization methods are applied to translate theinternal features into visually perceptible patterns for qualitative analysisof CNN layers. Many CNN visualization methods have been proposed in theComputer Vision literature to interpret the CNN network structure, operation,and semantic concept, yet applications to EEG data analysis have been limited.In this work we use 3 different methods to extract EEG-relevant features from aCNN trained on raw EEG data: optimal samples for each classification category,activation maximization, and reverse convolution. We applied these methods to ahigh-performing Deep Learning model with state-of-the-art performance for anEEG sex classification task, and show that the model features a difference inthe theta frequency band. We show that visualization of a CNN model can revealinteresting EEG results. Using these tools, EEG researchers using Deep Learningcan better identify the learned EEG features, possibly identifying new classrelevant biomarkers.

Assessing learned features of Deep Learning applied to EEG