Electroencephalography (EEG) biometrics has garnered significant attention in recent years owing to its non-intrusive nature, real-time detection capabilities, concealment, and high complexity. Despite these promising attributes, the practical deployment of EEG-based identity recognition systems remains hindered by limited cross-day recognition performance. While some studies have reported cross-day recognition, they often suffer from slow recognition speeds, failing to meet the basic requirements for practical applications. To address this issue, we propose an unsupervised domain adaptation algorithm based on spatial pattern alignment for visual-evoked potential (VEP)-based identity recognition. This method employs rotational alignment of spatial patterns to correct cross-day spatial filters and utilizes forward selection to identify optimal sub-bands. By utilizing this approach, significant improvements of speed and accuracy in cross-day recognition can be achieved. We validate the proposed algorithm on three existing VEP datasets: Dataset I (25 subjects across 30 days), Dataset II (21 subjects across 5 days), and Dataset III (15 subjects across 200 days). The results demonstrate a significant superiority over the compared algorithms. Furthermore, we conduct online experiments with 15 individuals across over 1000 days, and the outcomes remain consistent. Analyzing the dataset over nearly three years in terms of the temporal dimension, we observe evident performance differences caused by template aging effect: 30 days > 200 days > 1000 days. However, the proposed method effectively mitigates template aging, resulting in minimal performance differences among the various datasets. The introduced algorithm substantially enhances speed and accuracy in cross-day recognition, paving the way for the long-term stability and practicality of online brainwave recognition systems.

中文翻译：

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通过空间模式对齐实现基于 VEP 的身份识别的无监督域适应


近年来，脑电图（EEG）生物识别技术因其非侵入性、实时检测能力、隐蔽性和高复杂性而受到广泛关注。尽管有这些有前途的属性，但基于脑电图的身份识别系统的实际部署仍然受到有限的跨日识别性能的阻碍。虽然一些研究报告了跨日识别，但它们往往识别速度慢，无法满足实际应用的基本要求。为了解决这个问题，我们提出了一种基于空间模式对齐的无监督域适应算法，用于基于视觉诱发电位（VEP）的身份识别。该方法采用空间模式的旋转对齐来校正跨日空间滤波器，并利用前向选择来识别最佳子带。通过利用这种方法，可以显着提高跨日识别的速度和准确性。我们在三个现有的 VEP 数据集上验证了所提出的算法：数据集 I（30 天内 25 名受试者）、数据集 II（5 天内 21 名受试者）和数据集 III（200 天内 15 名受试者）。结果表明，与比较算法相比，具有显着的优越性。此外，我们对 15 个人进行了 1000 多天的在线实验，结果保持一致。从时间维度分析近三年的数据集，我们观察到模板老化效应导致的明显性能差异：30天> 200天> 1000天。然而，所提出的方法有效地减轻了模板老化，从而使不同数据集之间的性能差异最小。 引入的算法大幅提升了跨日识别的速度和准确度，为在线脑电波识别系统的长期稳定性和实用性奠定了基础。