# Deep Representation of EEG Signals Using Spatio-Spectral Feature Images
## 4 Results
### Deep Representation  
A selection of four typical SSFI patterns associated with deep representations in each of the CNV layers in one of the trained CNN models is shown in Figures 8 and 9. To facilitate their interpretation, each generated pattern is displayed as six separate topographical images corresponding to each of the frequency bands that define our SSFI structure. It is interesting to observe that the SSFI patterns associated with layer CNV1 are focused on large brain regions in different frequency bands. For example, the first topographic map in the theta (\(\theta\)) frequency band (first row, second column) shown in Figure 8a reveals a comparison between two halves of the brain. More specifically, if the two halves of the brain exhibit opposite activities in the (\(\theta\)) frequency band, this particular pattern is activated, i.e., the respective neuron in the CVN1 layer is activated). As described in [42], the orientation of a pattern should not be interpreted as fixed. Therefore, the second map of the first pattern in Figure 8a should be interpreted as a comparison between any two halves of the brain. Specifically, if any two halves of the brain exhibit opposite activities in the (\(\theta\)) frequency band, this particular neuron in CNV1 will be activated. Similar patterns are seen across filters corresponding to different frequency bands. In general, the patterns from layer CNV1, also known as low-level features, compare the brain activity between large regions of the brain. The existing literature that analyzes deep representations, such as [42], suggests that the generated patterns can have any orientation. This statement would apply to conventional scenarios, such as those including images of natural objects. However, SSFIs have a fixed orientation corresponding to a constant topography; hence, this allows us to interpret SSFI patterns exactly as they are revealed and exclude changes in orientation. For instance, an SSFI pattern highlighting the left and right halves of the brain should not be interpreted as any two halves of the brain. The top first map in the alpha (\(\alpha\)) frequency band shown in Figure 8a (first row, third column) reveals that the associated neuron focuses on the temporal lobe activity, more on the left temporal lobe than the right. Another example worth observing is the third pattern of the delta (\(\delta\)) frequency band (third row, first column). This pattern reveals that the associated neuron is focusing on the high activity of the frontal side of brain (FC5, F3, AF4, F4, and F8) and the low activity of the parietal side of brain (P7, O1, T8). In general, similar patterns are seen across filters corresponding to different frequency bands. In summary, these patterns demonstrate that layer CNV1 produces low-level features that compare the brain activity between large regions of the brain. In contrast, low-level features produced by conventional CNN models  
Figure 7: (**a**) Average performance of every trained CNN model when tested on one single subject, where the horizontal axis represents the test subject. (**b**) Average performance of each CNN model when tested on all the subjects, where the horizontal axis represents the training subject. for problems involving data structures such as images of the natural world correspond to edges and textures. Figure 8: Selection of typical SSFI deep representation patterns corresponding to four filters at layers (**a**) CNV1, (**b**) CNV2, and (**c**) CNV3 for subject S5. Each pattern is shown as a row, and consists of six maps corresponding to each frequency band arranged in columns. Compared to the SSFI patterns from layer CNV1, in layers CNV2 and CNV3, patterns are typically focused on very small regions. Layers CVN2 and CNV3 also present SSFIs that have dead channels. A dead channel is one which produces zero or constant response. Examples of dead channels are the ones producing the second, third, and sixth maps in the first pattern (first row) of Figure 8b.