A deep neural network and transfer learning combined method for cross-task classification of error-related potentials
Open accessAbstract
Background: Error-related potentials (ErrPs) are electrophysiological responses that naturally occur when humans perceive wrongdoing or encounter unexpected events. It offers a distinctive means of comprehending the error-processing mechanisms within the brain. A method for detecting ErrPs with high accuracy holds significant importance for various ErrPs-based applications, such as human-in-the-loop Brain-Computer Interface (BCI) systems. Nevertheless, current methods fail to fulfill the generalization requirements for detecting such ErrPs due to the high non-stationarity of EEG signals across different tasks and the limited availability of ErrPs datasets. Methods: This study introduces a deep learning-based model that integrates convolutional layers and transformer encoders for the classification of ErrPs. Subsequently, a model training strategy, grounded in transfer learning, is proposed for the effective training of the model. The datasets utilized in this study are available for download from the publicly accessible databases. Results: In cross-task classification, an average accuracy of about 78% was achieved, exceeding the baseline. Furthermore, in the leave-one-subject-out, within-session, and cross-session classification scenarios, the proposed model outperformed the existing techniques with an average accuracy of 71.81, 78.74, and 77.01%, respectively. Conclusions: Our approach contributes to mitigating the challenge posed by limited datasets in the ErrPs field, achieving this by reducing the requirement for extensive training data for specific target tasks. This may serve as inspiration for future studies that concentrate on ErrPs and their applications.
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CNN-transformer architectures have demonstrated competitive cross-subject ErrP performance under LOSO evaluation.
“CNN-transformer architectures have demonstrated competitive cross-subject ErrP performance under leave-one-subject-out (LOSO) evaluation [9], [10]”
From ERP-XTTN: interpretable prototype-guided cross-attention for cross-subject ERP classification (2026) · cites this paper as [10]
On BNCI, Ren et al. [10] reported a best per-session LOSO AUROC of 0.755 with two channels (FCz, Cz); ERP-XTTN achieved 0.771 with three channels under causal filtering.
“On BNCI, Ren et al. [10] reported a best per-session LOSO AUROC of 0.755 with two channels (FCz, Cz); ERP-XTTN achieved 0.771 with three channels under causal filtering.”
From ERP-XTTN: interpretable prototype-guided cross-attention for cross-subject ERP classification (2026) · cites this paper as [10]
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“On BNCI, Ren et al [10] reported a best per-session LOSO AUROC of 0.755 with two channels (FCz, Cz); ERP-XTTN achieved 0.771 with three channels under causal filtering.”
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- Authors
- Ren, G., Kumar, A., Mahmoud, S. S., & Fang, Q.
- Affiliations
- Shantou University (CN)
- Corresponding author
- Fang, Qiang
- Venue
- Frontiers in Human Neuroscience
- Publisher
- Frontiers Media SA
- Published
- 2024-06-12
- Volume
- 18
- Pages
- 1394107-1394107
- ISSN
- 1662-5161 (ISSN-L)
- PMC
- PMC11199896
- OpenAlex
- W4399594306
- Cited by
- 4
- References
- 56
- Citations by year
- 2026: 3 · 2025: 1
- FWCI
- 0.97
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Human Neuroscience (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- Li Ka Shing Foundation (2020LKSFG03C)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Keywords
- error-related potentials, EEG classification, cross-task generalization, transfer learning, brain-computer interface, error processing, limited training data
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