Brain responses related to working memory originate from distinct brain areas and oscillate at different frequencies. EEG signals with high temporal correlation can effectively capture these responses. Therefore, estimating the functional connectivity of EEG for working memory protocols in different frequency bands plays a significant role in analyzing the brain dynamics with increasing memory and cognitive loads, which remains largely unexplored. The present study introduces a Bayesian structure learning algorithm to learn the functional connectivity of EEG in sensor space. Next, the functional connectivity graphs are taken as input to the graph convolutional network to classify the working memory loads. The intrasubject (subject-specific) classification performed on 154 subjects for six different verbal working memory loads produced the highest classification accuracy of 96% and average classification accuracy of 89%, outperforming state-of-the-art classification models proposed in the literature. Furthermore, the proposed Bayesian structure learning algorithm is compared with state-of-the-art functional connectivity estimation methods through intersubject and intrasubject statistical analysis of variance. The results also show that the alpha and theta bands have better classification accuracy than the beta band.

翻译：与工作记忆相关的脑响应源于不同脑区并以不同频率振荡。具有高时间相关性的脑电图信号能有效捕捉这些响应。因此，针对不同频段的工作记忆协议，估计脑电图的功能连接对分析随记忆与认知负荷增加的脑动力学具有重要意义，相关研究尚不充分。本研究引入贝叶斯结构学习算法，在传感器空间学习脑电图的功能连接。随后，将功能连接图作为图卷积网络的输入，用于分类工作记忆负荷。针对154名受试者的六种不同言语工作记忆负荷进行受试者内（个体特异性）分类，最高分类准确率达96%，平均分类准确率为89%，优于文献中提出的最先进分类模型。此外，通过受试者间与受试者内的方差统计分析，将所提出的贝叶斯结构学习算法与最先进的功能连接估计方法进行比较。结果还表明，alpha频段和theta频段的分类准确率优于beta频段。