ArticleBrain and behavior2025
ESENA: A Novel Spatiotemporal Event Network Information Approach for Mining Scalp EEG Data.
Article in Brain and behavior, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
objectiveBrain 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.
methodsIn 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.
resultsFor 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.
conclusionOur 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.