Decoding of the neural representation of the visual RGB colour model

RGB colour 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 colour 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 colour stimuli than P300, a result that does not depend on a task-relevant paradigm, suggesting that RGB colour 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 colour in classification accuracy and channel location. Finally, space decoding of RGB colour 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.