Attention-ProNet: A Prototype Network with Hybrid Attention Mechanisms Applied to Zero Calibration in Rapid Serial Visual Presentation-Based Brain–Computer Interface
Open accessAbstract
The rapid serial visual presentation-based brain–computer interface (RSVP-BCI) system achieves the recognition of target images by extracting event-related potential (ERP) features from electroencephalogram (EEG) signals and then building target classification models. Currently, how to reduce the training and calibration time for classification models across different subjects is a crucial issue in the practical application of RSVP. To address this issue, a zero-calibration (ZC) method termed Attention-ProNet, which involves meta-learning with a prototype network integrating multiple attention mechanisms, was proposed in this study. In particular, multiscale attention mechanisms were used for efficient EEG feature extraction. Furthermore, a hybrid attention mechanism was introduced to enhance model generalization, and attempts were made to incorporate suitable data augmentation and channel selection methods to develop an innovative and high-performance ZC RSVP-BCI decoding model algorithm. The experimental results demonstrated that our method achieved a balance accuracy (BA) of 86.33% in the decoding task for new subjects. Moreover, appropriate channel selection and data augmentation methods further enhanced the performance of the network by affording an additional 2.3% increase in BA. The model generated by the meta-learning prototype network Attention-ProNet, which incorporates multiple attention mechanisms, allows for the efficient and accurate decoding of new subjects without the need for recalibration or retraining.
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Prototype-based approaches, including EPMN and Attention-ProNet, achieve zero-calibration cross-subject decoding in P300-based RSVP tasks by matching trials to class prototypes in a learned embedding space.
“Prototype-based approaches, including ERP Prototypical Matching Net (EPMN) [25] and Attention-ProNet [26], achieve zero-calibration cross-subject decoding in P300-based rapid serial visual presentation (RSVP) tasks by matching trials to class prototypes in a learned embedding space.”
From ERP-XTTN: interpretable prototype-guided cross-attention for cross-subject ERP classification (2026) · cites this paper as [26]
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- Authors
- Zhang, B., Xu, M., Zhang, Y., Ye, S., & Chen, Y.
- Affiliations
- Beijing Academy of Science and Technology (CN)
- Beijing University of Technology (CN)
- Beijing University of Posts and Telecommunications (CN)
- Beijing Institute of Mechanical Equipment, Beijing 100854, China
- Venue
- Bioengineering
- Publisher
- MDPI AG
- Published
- 2024-04-02
- Volume
- 11
- Issue
- 4
- Pages
- 347
- ISSN
- 2306-5354 (ISSN-L)
- PMC
- PMC11048110
- OpenAlex
- W4393409067
- Cited by
- 4
- References
- 32
- Citations by year
- 2026: 2 · 2025: 2
- FWCI
- 0.97
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Bioengineering (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- China Postdoctoral Science Foundation of China (2023M740171, 0420239352KF001-07)
- Talent Grant of the Beijing Academy of Science and Technology (2023M740171, 0420239352KF001-07)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Keywords
- rapid serial visual presentation, brain-computer interface, zero calibration, prototypical networks, meta-learning, hybrid attention mechanism, multi-scale attention, EEG feature extraction, event-related potentials, channel selection, data augmentation, cross-subject generalization
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