Investigating critical brain area for EEG-based binocular color fusion and rivalry with EEGNet.

Introduction: 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.

Methods: 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.

Results: 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.

Discussion: 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).