ESENA: A Novel Spatiotemporal Event Network Information Approach for Mining Scalp EEG Data

作者：Qiwei Dong, Runchen Yang, Xinrui Wang, Zongwen Feng, Chenggan Liu, Shiyu Chen, Yuxi Zhou, Dezhong Yao, Junru Ren, Qi Xu, Li Dong · 发表于：Brain and Behavior · 年份：2025 · DOI：10.1002/brb3.70426 · 被引用次数：1 · 研究领域：EEG and Brain-Computer Interfaces、Functional Brain Connectivity Studies、Emotion and Mood Recognition

OBJECTIVE: Brain activity possesses unique spatiotemporal characteristics. However, few electroencephalogram (EEG) analysis methods were designed to capture these features. Here, we developed a novel approach to mine spatiotemporal information contained in EEG data. METHODS: In this work, a novel approach, named EEG Spatiotemporal Event Network Analysis (ESENA), was proposed to fully capture the complex spatiotemporal patterns of EEG data during rich and complex stimulations. The essence of this method is to map power events onto network nodes and define connections on the basis of the temporal sequence of these events, thereby establishing a spatiotemporal network structure. Next, the performance and feasibility of ESENA were tested using three resting-state and game-playing state EEG datasets. RESULTS: For eyes-closed resting-state EEG, specific patterns of spatiotemporal event networks (SENs) were revealed by ESENA for different frequency bands, and the links between SENs were mainly located in regions of rhythmic activity revealed by the relative power spectrum. In the comparison between eyes-closed and eyes-open resting-state EEG, ESENA provided additional important spatiotemporal information in the delta frequency band in the frontal lobe, and in the theta frequency band in the frontoparietal lobes. In the comparison between the game-playing state and eyes-closed resting-state EEG, spatiotemporal information in the delta frequency band in the frontoparietal lobes, the t...