ESENA: A Novel Spatiotemporal Event Network Information Approach for Mining Scalp EEG Data - Qiwei Dong; Runchen Yang; Xinrui Wang; Zongwen Feng; Chenggan Liu; Shiyu Chen; Yuxi Zhou; Dezhong Yao; Junru Ren; Qi Xu; Li Dong (Brain and Behavior)
ABSTRACT 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 theta frequency band in the parietotemporal lobe and the alpha frequency band in the occipitoparietal lobes was additionally uncovered by ESENA. Moreover, these SENs were correlated with behavioral data. Conclusion Our findings demonstrated that the proposed ESENA method is superior to traditional EEG methods in discovering spatiotemporal patterns from EEG data and has the potential to become an important tool providing deeper insights into the brain's complex networks.