ERP prototypical matching net: a meta-learning method for zero-calibration RSVP-based image retrieval
Abstract
Objective. A rapid serial visual presentation (RSVP)-based brain-computer interface (BCI) is an efficient information detection technology through detecting event-related potentials (ERPs) evoked by target visual stimuli. The BCI system requires a time-consuming calibration process to build a reliable decoding model for a new user. Therefore, zero-calibration has become an important topic in BCI research. Approach. In this paper, we construct an RSVP dataset that includes 31 subjects, and propose a zero-calibration method based on a metric-based meta-learning: ERP prototypical matching net (EPMN). EPMN learns a metric space where the distance between electroencephalography (EEG) features and ERP prototypes belonging to the same category is smaller than that of different categories. Here, we employ prototype learning to learn a common representation from ERP templates of different subjects as ERP prototypes. Additionally, a metric-learning loss function is proposed for maximizing the distance between different classes of EEG and ERP prototypes and minimizing the distance between the same classes of EEG and ERP prototypes in the metric space. Main results. The experimental results showed that EPMN achieved a balanced-accuracy of 86.34% and outperformed the comparable methods. Significance. Our EPMN can realize zero-calibration for an RSVP-based BCI system.
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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 [25]
Cites
EEGNet: a compact convolutional neural network for EEG-based brain-computer interfaces
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“as minimum distance to Riemannian mean classifier (MDRM) [18], one convolution layer neural network (OCLNN) [19], and xDAWN [20], and RSVP decoding methods, such as hierarchical discriminant component analysis (HDCA) [21], Manor convolutional neural network (Manor-CNN) [22], and EEGNet [23].”
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A review of rapid serial visual presentation-based brain–computer interfaces
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Online and Offline Domain Adaptation for Reducing BCI Calibration Effort
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Zhang, B., Xu, M., Zhang, Y., Ye, S., & Chen, Y. (2024) · Bioengineering · doi:10.3390/bioengineering11040347 · Cited by 4 · see citing works
Self-citationERP-XTTN: interpretable prototype-guided cross-attention for cross-subject ERP classification
Genevier Wyman, C., & Hirshfield, L. (2026) · Journal of Neural Engineering · doi:10.1088/1741-2552/ae9f95
- Authors
- Wei, W., Qiu, S., Zhang, Y., Mao, J., & He, H.
- Affiliations
- Chinese Academy of Sciences (CN)
- Corresponding author
- Wei, Wei
- Qiu, Shuang
- Zhang, Yukun
- Mao, Jiayu
- He, Huiguang
- Venue
- Journal of Neural Engineering
- Publisher
- IOP Publishing
- Published
- 2022-04-01
- Volume
- 19
- Issue
- 2
- Pages
- 026028
- ISSN
- 1741-2552 (ISSN-L)
- 1741-2560
- OpenAlex
- W4220891365
- Cited by
- 24
- References
- 6
- Citations by year
- 2026: 2 · 2025: 9 · 2024: 8 · 2023: 2 · 2022: 3
- FWCI
- 2.83
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Journal of Neural Engineering (publishedVersion)
- PubMed (publishedVersion)
- 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
- zero calibration, rapid serial visual presentation, brain-computer interface, event-related potentials, meta-learning, metric learning, prototype learning, EEG features, image retrieval
- MeSH terms
- Calibration, Electroencephalography, Evoked Potentials, Humans, Learning, Brain-Computer Interfaces
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