Decoding Color Visual Working Memory from EEG Signals Using Graph Convolutional Neural Networks

解码方法 计算机科学 工作记忆 脑电图 人工智能 卷积神经网络 模式识别(心理学) 图形 认知 编码(内存) 颜色编码 心理学 神经科学 算法 理论计算机科学
作者
Xiaowei Che,Yuanjie Zheng,Xin Chen,Sutao Song,Shouxin Li
出处
期刊:International Journal of Neural Systems [World Scientific]
卷期号:32 (02) 被引量:17
标识
DOI:10.1142/s0129065722500034
摘要

Color has an important role in object recognition and visual working memory (VWM). Decoding color VWM in the human brain is helpful to understand the mechanism of visual cognitive process and evaluate memory ability. Recently, several studies showed that color could be decoded from scalp electroencephalogram (EEG) signals during the encoding stage of VWM, which process visible information with strong neural coding. Whether color could be decoded from other VWM processing stages, especially the maintaining stage which processes invisible information, is still unknown. Here, we constructed an EEG color graph convolutional network model (ECo-GCN) to decode colors during different VWM stages. Based on graph convolutional networks, ECo-GCN considers the graph structure of EEG signals and may be more efficient in color decoding. We found that (1) decoding accuracies for colors during the encoding, early, and late maintaining stages were 81.58%, 79.36%, and 77.06%, respectively, exceeding those during the pre-stimuli stage (67.34%), and (2) the decoding accuracy during maintaining stage could predict participants' memory performance. The results suggest that EEG signals during the maintaining stage may be more sensitive than behavioral measurement to predict the VWM performance of human, and ECo-GCN provides an effective approach to explore human cognitive function.
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