ArticleScientific reports2025
Multi-scale convolutional transformer network for motor imagery brain-computer interface.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- A Comprehensive Review of Sequence and Generative Models in Motor Imagery (MI) Classification for Brain-Computer Interfaces (BCIs).Sensors (Basel, Switzerland) · 2026Review
- A novel and accurate EEG emotion classification model based on multiple attention local binary patterns.BMC medicine · 2026Article
- PG-MCTFormer: A Prior-Guided Multi-Scale Convolutional Transformer for Interpretable Motor Imagery EEG Classification.Biomimetics (Basel, Switzerland) · 2026Article
- Continual-Learning-Enhanced CNN-Transformer Framework for Real-Time Motor-Imagery BCI in Virtual Environments.Bioengineering (Basel, Switzerland) · 2026Article
- Improving motor imagery decoding methods for an EEG-based mobile brain-computer interface in the context of the 2024 Cybathlon.Journal of neuroengineering and rehabilitation · 2026Article
- Adversarial robust EEG-based brain-computer interfaces using a hierarchical convolutional neural network.Scientific reports · 2026Article
- Multi-scale EEG feature decoding with Swin Transformers for subject independent motor imagery BCIs.Scientific reports · 2026Article
- Rehab-DRLX: explainable neurorehabilitation prognosis using deep reinforcement learning and transformer-based models.Frontiers in computational neuroscience · 2026Article
- APCformer: an aggregation-perception enhanced convolutional transformer network for MI-EEG decoding.Frontiers in neuroscience · 2026Article
- Temporal convolutional transformer for EEG based motor imagery decoding.Scientific reports · 2025Article
- RDPNet: A Multi-Scale Residual Dilated Pyramid Network with Entropy-Based Feature Fusion for Epileptic EEG Classification.Entropy (Basel, Switzerland) · 2025Article
- MCTGNet: A Multi-Scale Convolution and Hybrid Attention Network for Robust Motor Imagery EEG Decoding.Bioengineering (Basel, Switzerland) · 2025Article
- SVM-enhanced attention mechanisms for motor imagery EEG classification in brain-computer interfaces.Frontiers in neuroscience · 2025Article
- A hybrid Spiking Neural Network-Transformer architecture for motor imagery and sleep apnea detection.Frontiers in neuroscience · 2025Article
- TFANet: a temporal fusion attention neural network for motor imagery decoding.Frontiers in neuroscience · 2025Article
- Real-world evaluation of deep learning decoders for motor imagery EEG-based BCIs.Frontiers in systems neuroscience · 2025Article
- Transfer learning for subject-independent motor imagery EEG classification using convolutional relational networks.Frontiers in neuroscience · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Brain-computer interface (BCI) systems allow users to communicate with external devices by translating neural signals into real-time commands. Convolutional neural networks (CNNs) have been effectively utilized for decoding motor imagery electroencephalography (MI-EEG) signals in BCIs. However, traditional CNN-based methods face challenges such as individual variability in EEG signals and the limited receptive fields of CNNs. This study presents the Multi-Scale Convolutional Transformer (MSCFormer) model that integrates multiple CNN branches for multi-scale feature extraction and a Transformer module to capture global dependencies, followed by a fully connected layer for classification. The multi-branch multi-scale CNN structure effectively addresses individual variability in EEG signals, enhancing the model's generalization capabilities, while the Transformer encoder strengthens global feature integration and improves decoding performance. Extensive experiments on the BCI IV-2a and IV-2b datasets show that MSCFormer achieves average accuracies of 82.95% (BCI IV-2a) and 88.00% (BCI IV-2b), with kappa values of 0.7726 and 0.7599 in five-fold cross-validation, surpassing several state-of-the-art methods. These results highlight MSCFormer's robustness and accuracy, underscoring its potential in EEG-based BCI applications. The code has been released in https://github.com/snailpt/MSCFormer .
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