{
  "abstract": "An electroencephalogram (EEG) based brain-computer interface (BCI) enables direct communication between the brain and external devices. However, EEG-based BCIs face at least three major challenges in real-world applications: inaccurate decoding, poor robustness of EEG decoding models, and challenges in user privacy protection. While previous studies have addressed one or two of these issues, simultaneously accommodating all three challenges remains challenging and unexplored. This paper demonstrates for the first time that achieving simultaneously accurate, robust and privacy-preserving EEG decoding is feasible by proposing Privacy-preserving Adversarial Transfer (PAT). PAT integrates adversarial training into privacy-preserving transfer learning, achieving simultaneously accurate decoding, adversarial robustness, and privacy protection of EEG-based BCIs. Experiments on five public EEG datasets and three privacy-preserving scenarios (centralized source-free transfer, federated source-free transfer, and transfer with privacy-preserved source data) demonstrated that our proposed approach outperformed over 10 classic and state-of-the-art approaches in both accuracy and robustness. PAT even outperformed state-of-the-art transfer learning approaches that do not consider privacy protection. This is the first time three major challenges in EEG-based BCIs can be handled simultaneously. We hope to promote future research on more accurate, robust, and privacy-preserving EEG decoding.",
  "abstract_shown": true,
  "affiliations": [
    "Hubei Key Laboratory of Brain-inspired Intelligent Systems, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, 430074, China",
    "Shenzhen Huazhong University of Science and Technology Research Institute, Shenzhen, 518000, China",
    "Zhongguancun Academy, Beijing, 100094, China"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Xiaoqing Chen",
      "orcid": "0009-0004-3225-7449"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Tianwang Jia",
      "orcid": "0009-0000-1511-9395"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Dongrui Wu",
      "orcid": "0000-0002-7153-9703"
    },
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Dongrui Wu",
      "orcid": "0000-0002-7153-9703"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/c46013sqgrg5z.bib",
  "cited_by": 5,
  "doi": "10.1016/j.fmre.2026.04.034",
  "fulltext_url": null,
  "id": "c46013sqgrg5z",
  "issns": [
    "2096-9457",
    "2667-3258"
  ],
  "landing_url": "https://doi.org/10.1016/j.fmre.2026.04.034",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2805|EEG and Brain-Computer Interfaces"
    ]
  ],
  "openalex": "W4405469983",
  "pdf_url": "https://www.sciencedirect.com/science/article/pii/S2667325826004565/pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-06-01",
  "publisher": "Elsevier BV",
  "ris": "https://research.draconitic.com/paper/c46013sqgrg5z.ris",
  "sources": [
    {
      "first_seen": "2026-11-07T13:20:56Z",
      "native_id": "W4405469983",
      "source": "openalex",
      "url": "https://doi.org/10.1016/j.fmre.2026.04.034"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:2805|EEG and Brain-Computer Interfaces",
      "subfield": "neuroscience"
    }
  ],
  "title": "PAT: Privacy-Preserving Adversarial Transfer for Accurate, Robust and Privacy-Preserving EEG Decoding",
  "title_text": "PAT: Privacy-Preserving Adversarial Transfer for Accurate, Robust and Privacy-Preserving EEG Decoding",
  "type": "peer-reviewed",
  "updated_at": "2026-11-08T13:20:49Z",
  "url": "https://research.draconitic.com/paper/c46013sqgrg5z",
  "venue": "Fundamental Research",
  "versions": []
}