Unsupervised Domain Adaptation via Spatial Pattern Alignment for VEP-Based Identity Recognition

• Published in: IEEE internet of things journal Vol. 11; no. 20; pp. 33722 - 33733
• Main Authors: Zhao, Hongze, Wang, Yijun, Gao, Xiaorong
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 15.10.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Aging; Aging effect; Algorithms; biometrics; Classification algorithms; Correlation; domain adaptation; Electroencephalography; electroencephalography (EEG); Filter banks; Pattern recognition; Spatial filters; transfer learning
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2024.3431233

Electroencephalography (EEG) biometrics has garnered significant attention in recent years owing to its nonintrusive 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 data sets: 1) Data Set I (25 subjects across 30 days); 2) Data Set II (21 subjects across 5 days); and 3) Data Set 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 data set 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 data sets. 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.