# Machine Learning Based Color Classification by Means of Visually Evoked Potentials
###### Abstract  
Visually evoked potentials (VEPs) are widely used for diagnoses of different neurological diseases. Interestingly, there is limited research about the impact of the stimulus color onto the evoked response. Therefore, in our study we investigated the possibility of automatically classifying the stimulus color. The visual stimuli were selected to be red/black and green/black checkerboard patterns with equal light density. Both of these stimuli were presented in a random manner to nine subjects, while the electroencephalogram was recorded at the occipital lobe. After pre-processing and aligning the evoked potentials, an artificial neural network with one hidden layer was used to investigate the general possibility to automatically classify the stimulus color in three different settings. First, color classification with individually trained models, color classification with a common model, and color classification for each individual volunteer with a model trained on the data of the remaining subjects. With an average accuracy (ACC) of 0.83, the best results were achieved for the individually trained model. Also, the second (mean ACC = 0.76) and third experiments (mean ACC = 0.71) indicated a reasonable predictive accuracy across all subjects. Consequently, machine learning tools are able to appropriately classify stimuli colors based on VEPs. Although further studies are needed to improve the classification performance of our approach, this opens new fields of applications for VEPs. color classification; machine learning; visually evoked potential +
Footnote †: journal: Medicalhigh (i.e., in the range of ms) and the stimulus may be repeated several times to increase the signal quality; and third, the subject does not necessarily need to be conscious. In particular, the use of EPs during general anesthesia has increased their impact in clinical studies. Predicting post-operative visual output [5, 6] or estimating the depth of anesthesia based on AEPs are only two examples [7, 8]. Additionally, EPs are useful in follow-up and for monitoring the effect of a therapy or the disease course. Despite the fact, that several scientific issues concerning EPs have been investigated and (partly) answered, surprisingly in the case of VEPs, there are only a few studies about the impact of the stimulus color onto the recorded response [9, 10, 11, 12, 13, 14, 15]. In these studies, either event related transient VEPs or steady-state visually evoked potentials (SSVEPs) were investigated. The first type of stimulation results in a series of well-known deflections, illustrated in Figure 1a, triggered by a not-necessarily periodic stimulus, for instance checkerboard inversion, with a long enough pause between the single stimuli. In contrast to that, SSVEPs are induced by a higher frequent change of a specific stimulus leading to a sinusoidal-like brain response. The main findings of [9, 10, 11] showed that different colors lead to different brain responses, which was determined by investigating several frequency ranges of the steady-state visually evoked potentials (SSVEP)s. The authors of [10] found a relationship between the wavelength of the color and the impact onto the SSVEP, where colors with higher wavelength (e.g., red) tended to have a higher impact compared to colors with low wavelength (e.g., violet). Similar observations were made by [11], who investigated the brain responses to red/black, yellow/black, and green/black checkerboard patterns, as well as to the achromatic stimulus. Their study showed that, depending on the color and the intensity, the VEPs differ in latency and shape when averaging across 100 single trials. Going one step further, in our study we investigate whether it is possible to automatically classify the color based only on a few repetitions for which machine learning is well suited for. Machine learning has shown to be advantageous in several related fields. Impressive results in BCI research [16] or heartbeat classification for arrhythmia detection [17] are only two examples of many.