As a promising candidate to complement traditional biometric modalities, brain biometrics using electroencephalography (EEG) data has received a widespread attention in recent years. However, compared with existing biometrics such as fingerprints and face recognition, research on EEG biometrics is still in its infant stage. Most of the studies focus on either designing signal elicitation protocols from the perspective of neuroscience or developing feature extraction and classification algorithms from the viewpoint of machine learning. These studies have laid the ground for the feasibility of using EEG as a biometric verification modality, but they have also raised security and privacy concerns as EEG data contains sensitive information. Existing research has used hash functions and cryptographic schemes to protect EEG data, but they do not provide functions for revoking compromised templates as in cancellable template design. This paper proposes the first cancellable EEG template design for privacy-preserving EEG-based verification systems, which can protect raw EEG signals containing sensitive privacy information (e.g., identity, health and cognitive status). A novel cancellable EEG template is developed based on EEG features extracted by a deep learning model and a non-invertible transform. The proposed transformation provides cancellable templates, while taking advantage of EEG elicitation protocol fusion to enhance biometric performance. The proposed verification system offers superior performance than the state-of-the-art, while protecting raw EEG data. Furthermore, we analyze the system’s capacity for resisting multiple attacks, and discuss some overlooked but critical issues and possible pitfalls involving hill-climbing attacks, second attacks, and classification-based verification systems.

中文翻译：

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隐私保护脑电生物认证系统的可取消模板设计

作为补充传统生物识别方式的有希望的候选者，使用脑电图（EEG）数据的大脑生物识别近年来受到了广泛关注。然而，与现有的指纹、人脸识别等生物特征相​​比，脑电生物特征的研究还处于起步阶段。大多数研究要么从神经科学的角度设计信号诱导协议，要么从机器学习的角度开发特征提取和分类算法。这些研究为使用脑电图作为生物特征验证方式的可行性奠定了基础，但由于脑电图数据包含敏感信息，它们也引发了安全和隐私问题。现有研究使用哈希函数和加密方案来保护 EEG 数据，但它们不提供撤销受损模板的功能，如可取消模板设计。本文提出了第一个可取消的EEG模板设计，用于基于隐私保护的EEG验证系统，可以保护包含敏感隐私信息（例如，身份、健康和认知状态）的原始EEG信号。基于深度学习模型和不可逆变换提取的脑电图特征，开发了一种新的可取消脑电图模板。提议的转换提供了可取消的模板，同时利用 EEG 启发协议融合来增强生物识别性能。所提出的验证系统提供了比最先进的更优越的性能，同时保护原始 EEG 数据。此外，我们分析了系统抵抗多重攻击的能力，