ArticleScientific reports2024
CTNet: a convolutional transformer network for EEG-based motor imagery classification.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
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Who cites it
41 citing papers in PubMed.
- NeuroStream: spectral-spatio-temporal deep learning for visual stimulus classification from EEG.Scientific reports · 2026Article
- PDC-Net: A Prefrontal Dual-Channel Network with Gated Mamba Interaction for Cross-Subject Visual Attentional State Decoding.Brain sciences · 2026Article
- Deep Learning for Ear-EEG-Based Brain-Computer Interface: A Systematic Comparison and Design Insights.Biosensors · 2026Article
- A Dual-Branch Spatiotemporal Framework with Dynamic Weighted Permutation Entropy for Short-Window Motor Imagery EEG Decoding.Sensors (Basel, Switzerland) · 2026Article
- PG-MCTFormer: A Prior-Guided Multi-Scale Convolutional Transformer for Interpretable Motor Imagery EEG Classification.Biomimetics (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
- A Band-Aware Riemannian Network with Domain Adaptation for Motor Imagery EEG Signal Decoding.Brain sciences · 2026Article
- Entropy-Based Dual-Teacher Distillation for Efficient Motor Imagery EEG Classification.Entropy (Basel, Switzerland) · 2026Article
- EEG-TriNet++: A Transformer-Guided Meta-Learning Framework for Robust and Generalizable Motor Imagery Classification.Bioengineering (Basel, Switzerland) · 2026Article
- Article
- Maximizing Single-Feature Separability for Improving Transfer Learning in Motor Imagery EEG Decoding.Brain sciences · 2026Article
- Adversarial robust EEG-based brain-computer interfaces using a hierarchical convolutional neural network.Scientific reports · 2026Article
- CV-EEGNet: A Compact Complex-Valued Convolutional Network for End-to-End EEG-Based Emotion Recognition.Sensors (Basel, Switzerland) · 2026Article
- EEG-based dataset explicitly targets the transitions between sitting and standing for exploring neural activation patterns in motor imagery and execution.GigaScience · 2026Article
- Multi-scale EEG feature decoding with Swin Transformers for subject independent motor imagery BCIs.Scientific reports · 2026Article
- Leednet: a lightweight network for event detection in EEG signals.Journal on advances in signal processing · 2026Article
- A multi-dimensional CNN-Bi-GRU for IoT-based brain-computer interface in early epileptic seizure detection.Biology methods & protocols · 2026Article
- APCformer: an aggregation-perception enhanced convolutional transformer network for MI-EEG decoding.Frontiers in neuroscience · 2026Article
- Gaussian Connectivity-Driven EEG Imaging for Deep Learning-Based Motor Imagery Classification.Sensors (Basel, Switzerland) · 2025Article
- SATrans-Net: Sparse Attention Transformer for EEG-based motor imagery decoding.Scientific reports · 2025Article
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5 authors.
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Abstract
Brain-computer interface (BCI) technology bridges the direct communication between the brain and machines, unlocking new possibilities for human interaction and rehabilitation. EEG-based motor imagery (MI) plays a pivotal role in BCI, enabling the translation of thought into actionable commands for interactive and assistive technologies. However, the constrained decoding performance of brain signals poses a limitation to the broader application and development of BCI systems. In this study, we introduce a convolutional Transformer network (CTNet) designed for EEG-based MI classification. Firstly, CTNet employs a convolutional module analogous to EEGNet, dedicated to extracting local and spatial features from EEG time series. Subsequently, it incorporates a Transformer encoder module, leveraging a multi-head attention mechanism to discern the global dependencies of EEG's high-level features. Finally, a straightforward classifier module comprising fully connected layers is followed to categorize EEG signals. In subject-specific evaluations, CTNet achieved remarkable decoding accuracies of 82.52% and 88.49% on the BCI IV-2a and IV-2b datasets, respectively. Furthermore, in the challenging cross-subject assessments, CTNet achieved recognition accuracies of 58.64% on the BCI IV-2a dataset and 76.27% on the BCI IV-2b dataset. In both subject-specific and cross-subject evaluations, CTNet holds a leading position when compared to some of the state-of-the-art methods. This underscores the exceptional efficacy of our approach and its potential to set a new benchmark in EEG decoding.
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