RGB颜色模型
计算机科学
人工智能
解码方法
脑电图
模式识别(心理学)
频道(广播)
特征(语言学)
视皮层
神经解码
计算机视觉
心理学
神经科学
算法
语言学
哲学
计算机网络
作者
Yijia Wu,Yanjing Mao,Kaiqiang Feng,Donglai Wei,Liang Song
出处
期刊:PeerJ
[PeerJ]
日期:2023-05-11
卷期号:9: e1376-e1376
被引量:1
标识
DOI:10.7717/peerj-cs.1376
摘要
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.
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