School of Electronic Science and Engineering, Nanjing University, Nanjing 210023, China.
The Affiliated Tumor Hospital of Nantong University, Nantong 226361, China.
Comput Math Methods Med. 2022 Jun 28;2022:3878771. doi: 10.1155/2022/3878771. eCollection 2022.
Electroencephalogram (EEG) plays a crucial role in the study of working memory, which involves the complex coordination of brain regions. In this research, we designed and conducted series of experiments of memory with various memory loads or target forms and collected behavioral data as well as 32-lead EEG simultaneously. Combined with behavioral data analysis, we segmented EEG into slices; then, we calculated phase-locking value (PLV) of Gamma rhythms between every two leads, conducted binarization, constructed brain function network, and extracted three network characteristics of node degree, local clustering coefficient, and betweenness centrality. Finally, we inputted these network characteristics of all leads into support vector machines (SVM) for classification and obtained decent performances; i.e., all classification accuracies are greater than 0.78 on an independent test set. Particularly, PLV application was restricted to the narrow-band signals, and rare successful application to EEG Gamma rhythm, defined as wide as 30-100 Hz, had been reported. In order to address this limitation, we adopted simulation on band-pass filtered noise with the same frequency band as Gamma to help determine the PLV binarizing threshold. It turns out that network characteristics based on binarized PLV have the ability to distinguish the presence or absence of memory, as well as the intensity of the mental workload at the moment of memory. This work sheds a light upon phase-locking investigation between relatively wide-band signals, as well as memory research via EEG.
脑电图（EEG）在工作记忆研究中起着至关重要的作用，工作记忆涉及大脑区域的复杂协调。在这项研究中，我们设计并进行了一系列具有不同记忆负荷或目标形式的记忆实验，同时收集了行为数据和 32 导 EEG。结合行为数据分析，我们将 EEG 分段；然后，我们计算了每个导联之间伽马节律的锁相值（PLV），进行二值化，构建脑功能网络，并提取节点度、局部聚类系数和介数中心性三个网络特征。最后，我们将所有导联的这些网络特征输入支持向量机（SVM）进行分类，并获得了不错的性能；即在独立测试集中，所有分类准确率均大于 0.78。特别是，PLV 的应用仅限于窄带信号，很少有成功应用于 EEG 伽马节律的报道，伽马定义为 30-100 Hz 宽频带。为了解决这个限制，我们采用了与伽马相同频带的带通滤波噪声进行仿真，以帮助确定 PLV 二值化阈值。事实证明，基于二值化 PLV 的网络特征具有区分记忆存在或不存在以及记忆时心理工作负荷强度的能力。这项工作为相对宽带信号的锁相研究以及通过 EEG 进行的记忆研究提供了思路。
