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

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

SUBMITTER: Dong Q 

PROVIDER: S-EPMC11937924 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature