# Machine Learning Based Color Classification by Means of Visually Evoked Potentials
## 4 Discussion
### Interpretation of the Results  
In all subjects, it was possible to detect the color with which a visually evoked potential was triggered using the methods described above. This is shown by the reasonable values for ACC, AUC, and F1-Score. It can therefore be assumed that it will generally be possible to predict the color with which a visually evoked potential was triggered in a test person after prior training. Surprisingly, the scenario without training on the data from an individual patient resulted in comparable performance, indicating that the pattern of color-induced VEPs is somewhat similar between individual patients.  
However, we should note that the accuracy of this prediction was not equally high in all subjects (see Table 1). It is not completely clear why the classification performed better in some subjects than in others. Differences in the classification performance could be explained by different signal qualities and, thus, differences in signal purity. Variations in the fixation of electrodes could also play a role here, although strict care was taken to always place the electrodes to the same defined locations on the orbital skull. Nevertheless, conductivity between the electrodes and the skin possibly decreased during the measurement due to unintentional body/electrode movement. Unfortunately, electrode impedance was only checked in the beginning of the experiment; hence, a decreasing signal quality due to worse electrode impedance values is a possible reason, but cannot be verified. Therefore, for future studies, we suggest to regularly measure resistance during the experiment.  
In addition, different degrees of attention of the test persons could have influenced the results. Although all test persons were asked to focus on the stimuli as much as possible, it cannot be assumed that this was the case for all test persons to the same extent at all times during the measurement. Accordingly, any distraction by external stimuli could falsify the results.  
However, it could equally be true that there are subtle differences in the brain structures or in information processing of the individual participants caused by differences in experiences. In that context, a possible color blindness or red/green deficiency may also cause worse classification results. Although we defined such a deficiency as an exclusion criterion and included only participants with no known color deficiency, theoretically it may have happened that participants with a (minor) red/green weakness were included. To avoid this in future studies on this topic, it has to be assured that the subjects are color fit, either by examining the color fitness by an Anomalaskop or at least by carrying out a color vision test, such as the Ishihara or the Richmond Hardy Rand-Rittler pseudoisochromatic test [24].  
In general, it can be stated that characteristic times in the VEPs of all subjects were responsible for the classification. In particular, the steep rise of the N75 wave, as well as the areas around the P100 and N135 waves seem to play a major role (see Figure 3). The physiological/anatomical background of the VEPs with regard to color recognition is not yet known and should also be investigated in the future.