School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P. R. China.
Key Laboratory of Intelligent Computing & Information, Security in Universities of Shandong Shandong Provincial, Key Laboratory for Novel Distributed Computer Software, Technology Shandong Key Laboratory of Medical, Physics and Image Processing School of Information, Science and Engineering Institute of Biomedical Sciences, Shandong Normal University, Jinan 250358, P. R. China.
Int J Neural Syst. 2022 Feb;32(2):2250003. doi: 10.1142/S0129065722500034. Epub 2021 Dec 11.
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.
颜色在目标识别和视觉工作记忆 (VWM) 中起着重要作用。对人类大脑中颜色 VWM 的解码有助于理解视觉认知过程的机制并评估记忆能力。最近，有几项研究表明，在 VWM 的编码阶段，可以从头皮脑电图 (EEG) 信号中解码颜色，这一过程对可见信息进行了强有力的神经编码。颜色是否可以从其他 VWM 处理阶段（尤其是处理不可见信息的维持阶段）中解码，目前尚不清楚。在这里，我们构建了一个 EEG 颜色图卷积网络模型 (ECo-GCN) 来对不同 VWM 阶段的颜色进行解码。基于图卷积网络，ECo-GCN 考虑了 EEG 信号的图结构，在颜色解码方面可能更有效。我们发现：(1) 编码、早期和晚期维持阶段的颜色解码准确率分别为 81.58%、79.36%和 77.06%，超过了刺激前阶段（67.34%）；(2) 维持阶段的解码准确率可以预测参与者的记忆表现。结果表明，维持阶段的 EEG 信号可能比行为测量更能敏感地预测人类的 VWM 表现，而 ECo-GCN 提供了一种有效的方法来探索人类的认知功能。
