ReviewFrontiers in neuroscience2021
Topological Data Analysis as a New Tool for EEG Processing.
Review in Frontiers in neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Unsupervised functional stage detection from EEG using topological data analysis.Medical & biological engineering & computing · 2026Article
- Topological Data Analysis Based Characteristics of Electroencephalogram Signals in Children With Sleep Apnea.Journal of sleep research · 2025Article
- Article
- Who is WithMe? EEG features for attention in a visual task, with auditory and rhythmic support.Frontiers in neuroscience · 2024Article
- Statistical inference for dependence networks in topological data analysis.Frontiers in artificial intelligence · 2023Article
- Method for persistent topological features extraction of schizophrenia patients' electroencephalography signal based on persistent homology.Frontiers in computational neuroscience · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
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Abstract
Electroencephalography (EEG) is a widely used cerebral activity measuring device for both clinical and everyday life applications. In addition to denoising and potential classification, a crucial step in EEG processing is to extract relevant features. Topological data analysis (TDA) as an emerging tool enables to analyse and understand data from a different angle than traditionally used methods. As a higher dimensional analogy of graph analysis, TDA can model rich interactions beyond pairwise relations. It also distinguishes different dynamics of EEG time series. TDA remains largely unknown to the EEG processing community while it fits well the heterogeneous nature of EEG signals. This short review aims to give a quick introduction to TDA and how it can be applied to EEG analysis in various applications including brain-computer interfaces (BCIs). After introducing the objective of the article, the main concepts and ideas of TDA are explained. Next, how to implement it for EEG processing is detailed, and lastly the article discusses the benefits and limitations of the method.
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Registered trials
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