Decoding of the neural representation of the visual RGB color model

Abstract

RGB color is a basic visual feature. Here we use machine learning and visual evoked potential (VEP) of electroencephalogram (EEG) data to investigate the decoding features of the time courses and space location that extract it, and whether they depend on a common brain cortex channel. We show that RGB color information can be decoded from EEG data and, with the task-irrelevant paradigm, features can be decoded across fast changes in VEP stimuli. These results are consistent with the theory of both event-related potential (ERP) and P300 mechanisms. The latency on time course is shorter and more temporally precise for RGB color stimuli than P300, a result that does not depend on a task-relevant paradigm, suggesting that RGB color is an updating signal that separates visual events. Meanwhile, distribution features are evident for the brain cortex of EEG signal, providing a space correlate of RGB color in classification accuracy and channel location. Finally, space decoding of RGB color depends on the channel classification accuracy and location obtained through training and testing EEG data. The result is consistent with channel power value distribution discharged by both VEP and electrophysiological stimuli mechanisms.

Application of Machine Learning to Assess the Quality of Food Products—Case Study: Coffee Bean

“…In the case of the over-roasted coffee class, even organoleptic differences in brightness (L*), red-green (a*) and yellow-blue (b*) can be effectively discerned [34]. On the other hand, Table 2, in which the results of the RGB color space model are presented [37], shows that the under-roasted coffee class shows a statistical significance in the greatest differences between the other classes. In the case of this model, there are clearly statistically three different groups that show differences between each other [34].…”

“…The second step involved performing color analysis with the RGB (Red Green Blue) model, which is more popular in machine learning due to its digital solutions [37,38]. Data on the R, G, B components for the RGB model were extracted from digital images of each coffee research class.…”