Our restricted perceptual resources need a recognition mechanism to prioritize the differences between different colors based on the subjective judgement of RGB, the basic elements of color in visual color information.

In everyday circumstances, we are frequently accepted by a huge amount of visual color information interaction under complicated visual scenes.

We show that visual color EEG in V1 can reconstruct endocranial neuronal source location, through the machine learning decoding of channel location. We then explore the decoded results to trace the brain activity neural source location of EEG inversion problem and assess its reconstructive possibility. We first document the changes to brain color sensing using electroencephalography (EEG), and find that the sensing classification accuracy of primary visual cortex (V1) regions was positively correlated with the space correlation of visual evoked potential (VEP) power distribution under machine learning decoding. In this paper, we emulate the generation of visual color signal by task-irrelevant stimuli to activate brain neurons, where its consequences over the cerebral cortex is experimentally tracked.

However, the space location of the source generated by this neural mechanism remains elusive. Visual color sensing is generated by electrical discharges from endocranial neuronal sources that penetrate the skull and reach to the cerebral cortex.

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