College of Electronic and Optical Engineering & College of Microelectronics, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.
College of Automation and Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.
Int J Environ Res Public Health. 2022 Mar 17;19(6):3564. doi: 10.3390/ijerph19063564.
Working Memory (WM) is a short-term memory for processing and storing information. When investigating WM mechanisms using Electroencephalogram (EEG), its rhythmic synchronization properties inevitably become one of the focal features. To further leverage these features for better improve WM task performance, this paper uses a novel algorithm: Weight K-order propagation number (WKPN) to locate important brain nodes and their coupling characteristic in different frequency bands while subjects are proceeding French word retaining tasks, which is an intriguing but original experiment paradigm. Based on this approach, we investigated the node importance of PLV brain networks under different memory loads and found that the connectivity between frontal and parieto-occipital lobes in theta and beta frequency bands enhanced with increasing memory load. We used the node importance of the brain network as a feature vector of the SVM to classify different memory load states, and the highest classification accuracy of 95% is obtained in the beta band. Compared to the Weight degree centrality (WDC) and Weight Page Rank (WPR) algorithm, the SVM with the node importance of the brain network as the feature vector calculated by the WKPN algorithm has higher classification accuracy and shorter running time. It is concluded that the algorithm can effectively spot active central hubs so that researchers can later put more energy to study these areas where active hubs lie in such as placing Transcranial alternating current stimulation (tACS).
工作记忆（WM）是一种用于处理和存储信息的短期记忆。在使用脑电图（EEG）研究 WM 机制时，其节律同步特性不可避免地成为焦点特征之一。为了进一步利用这些特征来更好地提高 WM 任务的性能，本文使用了一种新颖的算法：权重 K 阶传播数（WKPN），以定位重要的大脑节点及其在不同频带中的耦合特征，而被试正在进行法语单词保持任务，这是一个有趣但原始的实验范式。基于这种方法，我们研究了不同记忆负荷下 PLV 脑网络的节点重要性，发现随着记忆负荷的增加，θ波和β波频带中额顶叶和顶枕叶之间的连通性增强。我们使用脑网络的节点重要性作为 SVM 的特征向量来对不同的记忆负荷状态进行分类，在β波段获得了最高 95%的分类准确率。与权重度中心性（WDC）和权重 PageRank（WPR）算法相比，使用 SVM 以 WKPN 算法计算的脑网络节点重要性作为特征向量的分类准确率更高，运行时间更短。结果表明，该算法可以有效地发现活跃的中心枢纽，以便研究人员以后可以投入更多精力研究这些活跃枢纽所在的区域，例如放置经颅交流电刺激（tACS）。
