{
  "abstract": "Network neuroscience has traditionally relied on node-centric approaches, which are inherently limited in capturing the brain's overlapping functional architectures and higher-order edge-to-edge interactions. The emerging edge-centric paradigm addresses this gap by shifting the analytical unit from nodes to edges. However, while edge-centric models have been established for DTI and fMRI, comparable EEG methodologies have yet to be developed. In this study, we proposed a Phase-based Edge-centric Coupling (PEC) algorithm for EEG network modelling, where edge time series are derived from instantaneous phase differences of EEG signals and edge-to-edge coupling is quantified via normalized inner product. Simulation tests demonstrated the robustness of PEC algorithm, achieving high accuracy (<10% relative error) in coupling estimation and discriminative capacity (AUROC>0.7) against spurious coupling.When provided sufficient data length(>=16 cycles), PEC outperformed traditional Phase Node-centric Coupling (PNC) in both individual identifiability and cognitive-state identifiability, enabling superior characterization of stable individual neural signatures and dynamic task-dependent functional configurations. Furthermore, unlike PNC's hard-partitioned modularity, PEC captured highly overlapping yet functionally specialized modular structures and connector hubs, aligning with task-engaged cognitive processes. Applied to dyadic interactions, PEC identified distinct cross-brain modules encoding symmetric and asymmetric neural coordination. These findings establish PEC as a valid and powerful tool for EEG network modelling, providing new insights into the brain's complex, multifunctional organization at both intra-brain and inter-brain networks.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.32286",
  "author_total": 5,
  "authors": [
    {
      "name": "Chengcheng Wu"
    },
    {
      "name": "Zichen Shi"
    },
    {
      "name": "Jiayang Xu"
    },
    {
      "name": "Shanbao Tong"
    },
    {
      "name": "Xiaoli Guo"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/agmbtrx8q4k60.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.32286",
  "fulltext_url": null,
  "id": "agmbtrx8q4k60",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.32286",
  "language": null,
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "q-bio.NC"
    ],
    [
      "openalex",
      "2805|Functional Brain Connectivity Studies"
    ]
  ],
  "openalex": "W7223657496",
  "pdf_url": "https://arxiv.org/pdf/2610.32286",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-23",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/agmbtrx8q4k60.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:35:20Z",
      "native_id": "2610.32286",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.32286"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.NC",
      "subfield": "neuroscience"
    }
  ],
  "title": "Phase-based Edge Coupling: Modeling Overlapping Functional Architectures and High-Order Edge-to-Edge Interactions in EEG Networks",
  "title_text": "Phase-based Edge Coupling: Modeling Overlapping Functional Architectures and High-Order Edge-to-Edge Interactions in EEG Networks",
  "type": "preprint",
  "updated_at": "2026-10-30T12:35:20Z",
  "url": "https://research.draconitic.com/paper/agmbtrx8q4k60",
  "venue": "arXiv",
  "versions": []
}