Lv Zhineng, Liu Xiang, Dai Mengshi, Jin Xuesong, Huang Xiaoqiao, Chen Zaiqing
School of Information Science and Technology, Yunnan Normal University, Kunming, China.
Engineering Research Center of Computer Vision and Intelligent Control Technology, Yunnan Provincial Department of Education, Kunming, China.
Front Neurosci. 2024 Feb 27;18:1361486. doi: 10.3389/fnins.2024.1361486. eCollection 2024.
Binocular color fusion and rivalry are two specific phenomena in binocular vision, which could be used as experimental tools to study how the brain processes conflicting information. There is a lack of objective evaluation indexes to distinguish the fusion or rivalry for dichoptic color.
This paper introduced EEGNet to construct an EEG-based model for binocular color fusion and rivalry classification. We developed an EEG dataset from 10 subjects.
By dividing the EEG data from five different brain areas to train the corresponding models, experimental results showed that: (1) the brain area represented by the back area had a large difference on EEG signals, the accuracy of model reached the highest of 81.98%, and more channels decreased the model performance; (2) there was a large effect of inter-subject variability, and the EEG-based recognition is still a very challenge across subjects; and (3) the statistics of EEG data are relatively stationary at different time for the same individual, the EEG-based recognition is highly reproducible for an individual.
The critical channels for EEG-based binocular color fusion and rivalry could be meaningful for developing the brain computer interfaces (BCIs) based on color-related visual evoked potential (CVEP).
双眼颜色融合和双眼竞争是双眼视觉中的两种特定现象，可作为实验工具来研究大脑如何处理冲突信息。目前缺乏用于区分双眼分视颜色的融合或竞争的客观评估指标。
本文引入EEGNet来构建基于脑电图的双眼颜色融合和竞争分类模型。我们从10名受试者开发了一个脑电图数据集。
通过划分来自五个不同脑区的脑电图数据来训练相应模型，实验结果表明：（1）后部区域所代表的脑区在脑电图信号上有很大差异，模型准确率最高达到81.98%，且更多通道会降低模型性能；（2）个体间差异影响很大，基于脑电图的识别在不同个体间仍然是一项极具挑战性的任务；（3）同一受试者在不同时间的脑电图数据统计相对稳定，基于脑电图的识别对个体具有高度可重复性。
基于脑电图的双眼颜色融合和竞争的关键通道对于开发基于颜色相关视觉诱发电位（CVEP）的脑机接口（BCI）可能具有重要意义。
