Decoding of the neural representation of the visual RGB color model

Yijia Wu; Yanjing Mao; Kaiqiang Feng; Donglai Wei; Liang Song

doi:10.7717/peerj-cs.1376

Decoding of the neural representation of the visual RGB color model

PeerJ Comput Sci. 2023 May 11:9:e1376. doi: 10.7717/peerj-cs.1376. eCollection 2023.

Authors

Yijia Wu^{1

2}, Yanjing Mao¹, Kaiqiang Feng¹, Donglai Wei¹, Liang Song^{1

2}

Affiliations

¹ Fudan University, Fudan University, ShangHai, YangPu, China.
² Shanghai Key Research Laboratory, Shanghai Key Research Laboratory, ShangHai, PuDong, China.

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.

Keywords: Color; Decoding; EEG; Machine learning; RGB; Visual.

Grants and funding

This work has been financially supported by the National Key Research and Development Program of China (2018YFC0831102), the Science and Technology Commission of Shanghai Municipality (21JC1405300), the Academy for engineering & technology of Fudan University, and the Shanghai Key Research Laboratory of INSAI. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.