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Cited by 1EEG and Brain-Computer InterfacesEmotion and Mood RecognitionGaze Tracking and Assistive Technology

A Transformer Encoder and Convolutional Neural Network Combined Method for Classification of Error-related Potentials

Ren, Guihong, Mahmoud, Seedahmed S., Kumar, Akshay, Fang, Qiang, Yu, Boyuan·2023·2023 IEEE Biomedical Circuits and Systems Conference (BioCAS)
DOI 10.1109/biocas58349.2023.10389146
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Abstract

Background and Objectives. As an inherent feedback mechanism in humans, Error-related Potentials (ErrPs) signals have received increasing attention for their potential to improve the accuracy and efficiency of Brain-computer interface (BCI) systems. However, the successful detection of ErrPs signals in a single trial is a great challenge due to the low signal-to-noise ratio (SNR) and the high individual differences. In this study, we propose a novel method that combines convolutional layers and transformer encoder modules to classify ErrPs from electroencephalogram (EEG) signals. Methods. The raw data from all electrode channels were chosen as the inputs of the proposed method and two different dataset partition ways, which include within-session and cross-subject, were used to test the proposed model. The dataset used for evaluation is the ‘Monitoring error-related potentials’ dataset of the BNCI Horizon 2020. Results. For within-session classification, a mean accuracy of 88.50%, an AUROC of 0.95, and a F1-score of 0.82 using stratified 10-fold cross-validation were achieved. For cross-subject classification, the average accuracy, AUROC, and Fl-score have reached 82.89%, 0.90, and 0.71 respectively. The results obtained outperformed the state-of-the-art methods. Conclusions. The results showed the ability of transformer-based deep learning (DL) method in detecting ErrPs signals from low SNR EEG data and shed light on improving the performance of BCI systems.

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Crossref · 10.1109/biocas58349.2023.10389146

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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 [9]

  • All results reported here use causal IIR filtering under calibration-free LOSO evaluation; many published LOSO benchmarks use acausal filtering instead, so their reported performance is not reproducible in a forward-processing pipeline.

    “All results reported here use causal IIR filtering under calibration-free LOSO evaluation; many published LOSO benchmarks use acausal filtering instead [9], [16], so their reported performance is not reproducible in a forward-processing pipeline.”

    From ERP-XTTN: interpretable prototype-guided cross-attention for cross-subject ERP classification (2026) · cites this paper as [9]

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Related

Authors
Ren, G., Mahmoud, S. S., Kumar, A., Fang, Q., & Yu, B.
Affiliations
Shantou University (CN)
ORCID
Venue
2023 IEEE Biomedical Circuits and Systems Conference (BioCAS)
Publisher
IEEE
Published
2023-10-19
Volume
30
Pages
1-5
DOI
10.1109/biocas58349.2023.10389146
OpenAlex
W4390993526
Cited by
0
References
31
FWCI
0.00
Type
conference paper
Language
en
Open access
closed
Licence
Locations
IEEE Biomedical Circuits and Systems Conference (BioCAS) (publishedVersion)
Funders
Li Ka Shing Foundation
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, Transformer encoder, convolutional neural networks, CNN-Transformer hybrid, brain-computer interface, low signal-to-noise ratio, cross-subject classification

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