Gu Chengxian, Jin Xuanyu, Zhu Li, Yi Hangjie, Liu Honggang, Yang Xinyu, Babiloni Fabio, Kong Wanzeng
School of Computer Science, Hangzhou Dianzi University, Hangzhou, 310018 Zhejiang China.
Zhejiang Key Laboratory of Brain-Machine Collaborative Intelligence, Hangzhou, 310018 Zhejiang China.
Cogn Neurodyn. 2025 Dec;19(1):15. doi: 10.1007/s11571-024-10192-z. Epub 2025 Jan 9.
Brainprint recognition technology, regarded as a promising biometric technology, encounters challenges stemming from the time-varied, low signal-to-noise ratio of brain signals, such as electroencephalogram (EEG). Steady-state visual evoked potentials (SSVEP) exhibit high signal-to-noise ratio and frequency locking, making them a promising paradigm for brainprint recognition. Consequently, the extraction of time-invariant identity information from SSVEP EEG signals is essential. In this paper, we propose an Attentive Multi-sub-band Depth Identity Embedding Learning Network for stable cross-session SSVEP brainprint recognition. To address the issue of low recognition accuracy across sessions, we introduce the Sub-band Attentive Frequency mechanism, which integrates the frequency-domain relevant characteristics of the SSVEP paradigm and focuses on exploring depth-frequency identity embedding information. Also, we employ Attentive Statistic Pooling to enhance the stability of frequency domain feature distributions across sessions. Extensive experimentation and validation were conducted on two multi-session SSVEP benchmark datasets. The experimental results show that our approach outperforms other state-of-art models on 2-second samples across sessions and has the potential to serve as a benchmark in multi-subject biometric recognition systems.
脑纹识别技术作为一种很有前景的生物识别技术，面临着诸如脑电图（EEG）等脑信号时变、低信噪比所带来的挑战。稳态视觉诱发电位（SSVEP）具有高信噪比和频率锁定特性，使其成为脑纹识别的一个有前景的范例。因此，从SSVEP脑电信号中提取时不变身份信息至关重要。在本文中，我们提出了一种用于稳定跨会话SSVEP脑纹识别的注意力多子带深度身份嵌入学习网络。为了解决跨会话识别准确率低的问题，我们引入了子带注意力频率机制，该机制整合了SSVEP范式的频域相关特征，并专注于探索深度频率身份嵌入信息。此外，我们采用注意力统计池化来增强跨会话频域特征分布的稳定性。在两个多会话SSVEP基准数据集上进行了广泛的实验和验证。实验结果表明，我们的方法在跨会话的2秒样本上优于其他现有模型，并且有潜力成为多主体生物识别系统中的一个基准。
