# Deep Representation of EEG Signals Using Spatio-Spectral Feature Images
## 5 Conclusions and Discussion  
Modern DNNs have become a popular approach in EEG studies [18; 19; 20; 21]. From a computational point of view, trained DNNs are pipelines that produce multiple representations of their input, which are known as deep representations. Each deep representation encapsulates a set of features describing the input. Representations close to the input of trained DNNs are associated with low-level features, whereas deeper representations close to the output correspond to high-level features and can identify complex patterns in the DNN input. Despite the promising results shown by DNN approaches in EEG studies, our understanding of the computational pipelines that they implement remains limited. Understanding DNN pipelines and the deep representations that they produce can be useful. First, this would allow us to identify which EEG patterns are relevant for a specific prediction, which could shed light on the brain mechanisms involved. Second, by comparing deep representations from DNNs trained on EEG from different individuals, we can identify similarities and differences that could suggest different brain mechanisms. This would in turn allow us to cluster individuals according to the brain mechanisms that they exhibit. Third, understanding DNN pipelines and deep representations could contribute to realizing effective transfer learning and reduce training time. Transfer learning approaches can be used to retrain an existing DNN so that it can be used on other individuals or can be applied to predictive tasks different from the one for which the DNN was originally trained. In this article, we have presented an approach that allows us to interpret the deep representations of DNN models trained on EEG multi-channel data. The proposed approach exploits the spatio-spectral relationship of EEG data and generates SSFI patterns revealing the frequency bands and the spatial region of brain that activate the neurons within each layer in a DNN model. The association between brain regions and frequency bands and physical, physiological, and psychological activities is widely accepted in neuroscience, and hence, SSFI patterns provide us with meaningful interpretations of the deep representations produced by DNN models. SSFI patterns interpreting deep representations can therefore enhance our understanding of the brain activity. With BCI systems in mind, SSFI patterns can also contribute to the design and realization of transfer learning. To evaluate our approach, we used the PhyAAt dataset for auditory tasks. We trained 25 CNN models on EEG multi-channel signals from each subject in the dataset. First, we analyzed the performance of each individual CNN model on EEG signals from the subject it was trained on. Then we conducted ISD analysis, which allowed us to investigate how a model trained on one subject performs on others. The observed variation in performance demonstrates the importance of learning robust deep representations, which are necessary to implement transfer learning effectively. Figure 11: Average saliency map of listening task for all 25 subjects. We analyzed the SSFI patterns associated with the deep representation generated by neurons in each of the layers of the trained CNN models. To generate SSFI patterns, we used an activation maximization approach. SSFI saliency maps for listening, writing, and resting tasks were also produced and averaged saliency maps were obtained to highlight the common brain regions that are associated with listening tasks for all the subjects. Our approach and subsequent analysis reveals that, unlike conventional CNN scenarios such as those involving images of the natural world, SSFI low-level features represent the activities in larger brain regions and high-level features represent clusters of small brain regions. Interestingly, middle-level features are used to selectively combine different frequency bands.