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<p>With the fusion development of neuroscience, biotechnology, and computer science, the brain-computer interface (BCI) has become a worldwide emerging technology. Electroencephalogram (EEG) signals, as the core data type, play a crucial role in promoting technological innovation. However, EEG data possess both biometric identification attributes and mental activity mapping functions, and face significant risks of intellectual property infringement and abuse during open sharing. Existing intellectual property protection technologies have limitations in balancing data openness and security. To address these issues, this work proposes a novel intellectual property protection method called DWSM based on digital watermarking and statistical testing. DWSM designs a closed-loop “embedding-authentication” copyright protection technical system, including an adaptive EEG signal digital watermark embedding method based on wavelet transform and hash algorithm, a hypothesis testing authentication method for scenarios where prediction probabilities are accessible, and a non-parametric testing authentication method for scenarios where only classification labels are obtainable. Experimental validation using public EEG datasets such as SEED-IV and THU-RSVP, demonstrates that the proposed method achieves effective copyright tracing while ensuring the usability of EEG data, providing technical support for the compliant and secure sharing of EEG data.</p>

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DWSM: an EEG digital watermark generation and black-box authentication method based on wavelet transform and statistical hypothesis testing

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月29日

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  • Qian Zhong,
  • Pei Wen,
  • Yong Wei,
  • Ting Wang,
  • Hongxin Li

摘要

With the fusion development of neuroscience, biotechnology, and computer science, the brain-computer interface (BCI) has become a worldwide emerging technology. Electroencephalogram (EEG) signals, as the core data type, play a crucial role in promoting technological innovation. However, EEG data possess both biometric identification attributes and mental activity mapping functions, and face significant risks of intellectual property infringement and abuse during open sharing. Existing intellectual property protection technologies have limitations in balancing data openness and security. To address these issues, this work proposes a novel intellectual property protection method called DWSM based on digital watermarking and statistical testing. DWSM designs a closed-loop “embedding-authentication” copyright protection technical system, including an adaptive EEG signal digital watermark embedding method based on wavelet transform and hash algorithm, a hypothesis testing authentication method for scenarios where prediction probabilities are accessible, and a non-parametric testing authentication method for scenarios where only classification labels are obtainable. Experimental validation using public EEG datasets such as SEED-IV and THU-RSVP, demonstrates that the proposed method achieves effective copyright tracing while ensuring the usability of EEG data, providing technical support for the compliant and secure sharing of EEG data.