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.

Phase-locking between local field potential and spikes has been widely investigated through neurophysiological analysis. Two indices most frequently adopted traditionally include the mean resultant length and p of the Rayleigh test. However, the traditional method is found less reliable for cases with substantial sample sizes or with bimodal distribution. In this article, K we propose an entropy-based method to detect and measure phase-locking. Simulation results prove that the method can be effectively applied to cases with a relatively large sample size and it can effectively detect phase-locking even under bimodal conditions with two peaks of opposite phases. Furthermore, we propose thresholds to help measure the intensity of spike field phase-locking. We also apply the proposed method to our experimental data and find phase locking with two opposite phases, which would be left out in traditional phase-locking analysis.