ArticleSensors (Basel, Switzerland)2021
Multilayer Network Approach in EEG Motor Imagery with an Adaptive Threshold.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- EEG Signal Processing for Biomedical Applications.Sensors (Basel, Switzerland) · 2022Article
- Validation of Continuous Monitoring System for Epileptic Users in Outpatient Settings.Sensors (Basel, Switzerland) · 2022Article
- EEG emotion recognition based on cross-frequency granger causality feature extraction and fusion in the left and right hemispheres.Frontiers in neuroscience · 2022Article
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Authors and funding
5 authors.
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Abstract
The brain has been understood as an interconnected neural network generally modeled as a graph to outline the functional topology and dynamics of brain processes. Classic graph modeling is based on single-layer models that constrain the traits conveyed to trace brain topologies. Multilayer modeling, in contrast, makes it possible to build whole-brain models by integrating features of various kinds. The aim of this work was to analyze EEG dynamics studies while gathering motor imagery data through single-layer and multilayer network modeling. The motor imagery database used consists of 18 EEG recordings of four motor imagery tasks: left hand, right hand, feet, and tongue. Brain connectivity was estimated by calculating the coherence adjacency matrices from each electrophysiological band (δ, θ, α and β) from brain areas and then embedding them by considering each band as a single-layer graph and a layer of the multilayer brain models. Constructing a reliable multilayer network topology requires a threshold that distinguishes effective connections from spurious ones. For this reason, two thresholds were implemented, the classic fixed (average) one and Otsu's version. The latter is a new proposal for an adaptive threshold that offers reliable insight into brain topology and dynamics. Findings from the brain network models suggest that frontal and parietal brain regions are involved in motor imagery tasks.
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Registered trials
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