TSFAN: tensorized spatial-frequency attention network with domain adaptation for cross-session EEG-based biometric recognition

• Published in: Journal of neural engineering Vol. 21; no. 4; pp. 46005 - 46019
• Main Authors: Jin, Xuanyu, Yang, Xinyu, Kong, Wanzeng, Zhu, Li, Tang, Jiajia, Peng, Yong, Ding, Yu, Zhao, Qibin
• Format: Journal Article
• Language: English
• Published: England IOP Publishing 01.08.2024
• Subjects: Adult; Attention - physiology; Biometric Identification - methods; domain adaptation; EEG-based biometric recognition; Electroencephalography - methods; Female; Humans; Male; Neural Networks, Computer; tensor network; Young Adult; domain adaptation; EEG-based biometric recognition; tensor network
• ISSN: 1741-2560; 1741-2552; 1741-2552
• DOI: 10.1088/1741-2552/ad5761

Objective. Electroencephalogram (EEG) signals are promising biometrics owning to their invisibility, adapting to the application scenarios with high-security requirements. However, It is challenging to explore EEG identity features without the interference of device and state differences of the subject across sessions. Existing methods treat training sessions as a single domain, affected by the different data distribution among sessions. Although most multi-source unsupervised domain adaptation (MUDA) methods bridge the domain gap between multiple source and target domains individually, relationships among the domain-invariant features of each distribution alignment are neglected. Approach. In this paper, we propose a MUDA method, Tensorized Spatial-Frequency Attention Network (TSFAN), to assist the performance of the target domain for EEG-based biometric recognition. Specifically, significant relationships of domain-invariant features are modeled via a tensorized attention mechanism. It jointly incorporates appropriate common spatial-frequency representations of pairwise source and target but also cross-source domains, without the effect of distribution discrepancy among source domains. Additionally, considering the curse of dimensionality, our TSFAN is approximately represented in Tucker format. Benefiting the low-rank Tucker Network, the TSFAN can scale linearly in the number of domains, providing us the great flexibility to extend TSFAN to the case associated with an arbitrary number of sessions. Main results. Extensive experiments on the representative benchmarks demonstrate the effectiveness of TSFAN in EEG-based biometric recognition, outperforming state-of-the-art approaches, as verified by cross-session validation. Significance. The proposed TSFAN aims to investigate the presence of consistent EEG identity features across sessions. It is achieved by utilizing a novel tensorized attention mechanism that collaborates intra-source transferable information with inter-source interactions, while remaining unaffected by domain shifts in multiple source domains. Furthermore, the electrode selection shows that EEG-based identity features across sessions are distributed across brain regions, and 20 electrodes based on 10–20 standard system are able to extract stable identity information.