Electroencephalogram(EEG)-based authentication has received increasing attention from researchers as they believe it could serve as an alternative to more conventional personal authentication methods. Unfortunately, EEG signals are non-stationary and could be easily contaminated by noise and artifacts. Therefore, further processing of data analysis is needed to retrieve useful information. Various machine learning approaches have been proposed and implemented in the EEG-based domain, with deep learning being the most current trend. However, retaining the performance of a deep learning model requires substantial computational effort and a vast amount of data, especially when the models go deeper to generate consistent results. Deep learning models trained with small data sets from scratch may experience an overfitting issue. Transfer learning becomes an alternative solution. It is a technique to recognize and apply the knowledge and skills learned from the previous tasks to a new domain with limited training data. This study attempts to explore the applicability of transferring various pre-trained models’ knowledge to the EEG-based authentication domain. A self-collected database that consists of 30 subjects was utilized in the analysis. The database enrolment is divided into two sessions, with each session producing two sets of EEG recording data. The frequency spectrums of the preprocessed EEG signals are extracted and fed into the pre-trained models as the input data. Three experimental tests are carried out and the best performance is reported with accuracy in the range of 99.1–99.9%. The acquired results demonstrate the efficiency of transfer learning in authenticating an individual in this domain.

中文翻译：

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基于脑电图的身份验证中迁移学习模型的评估

基于脑电图（EEG）的身份验证越来越受到研究人员的关注，因为他们相信它可以作为更传统的个人身份验证方法的替代方案。不幸的是，脑电图信号是非平稳的，很容易受到噪声和伪影的污染。因此，需要对数据进行进一步处理分析以检索有用的信息。在基于脑电图的领域中已经提出并实施了各种机器学习方法，其中深度学习是最新的趋势。然而，保持深度学习模型的性能需要大量的计算工作和大量的数据，特别是当模型更深入以生成一致的结果时。从头开始使用小数据集训练的深度学习模型可能会遇到过度拟合问题。迁移学习成为一种替代解决方案。它是一种利用有限的训练数据识别和应用从先前任务中学到的知识和技能到新领域的技术。本研究试图探索将各种预训练模型的知识转移到基于脑电图的身份验证领域的适用性。分析中使用了由 30 名受试者组成的自行收集的数据库。数据库登记分为两个会话，每个会话产生两组脑电图记录数据。提取预处理的脑电图信号的频谱并将其作为输入数据输入到预训练模型中。进行了三项实验测试，报告了最佳性能，准确度在 99.1–99.9% 范围内。获得的结果证明了迁移学习在该领域验证个人身份的效率。