ArticleScientific reports2024
Compact convolutional transformer for subject-independent motor imagery EEG-based BCIs.
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 6 papers.
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Who cites it
6 citing papers in PubMed.
- Cardio amyloid-artificial intelligence: advanced multi-modal screening for transthyretin cardiac amyloidosis in severe aortic stenosis patients.European heart journal. Digital health · 2026Article
- PG-MCTFormer: A Prior-Guided Multi-Scale Convolutional Transformer for Interpretable Motor Imagery EEG Classification.Biomimetics (Basel, Switzerland) · 2026Article
- Multi-scale EEG feature decoding with Swin Transformers for subject independent motor imagery BCIs.Scientific reports · 2026Article
- Transformers in EEG Analysis: A Review of Architectures and Applications in Motor Imagery, Seizure, and Emotion Classification.Sensors (Basel, Switzerland) · 2025Review
- Motor imagery EEG classification via wavelet-packet synthetic augmentation and entropy-based channel selection.Frontiers in 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
3 authors.
Funding
Abstract
Motor imagery electroencephalography (EEG) analysis is crucial for the development of effective brain-computer interfaces (BCIs), yet it presents considerable challenges due to the complexity of the data and inter-subject variability. This paper introduces EEGCCT, an application of compact convolutional transformers designed specifically to improve the analysis of motor imagery tasks in EEG. Unlike traditional approaches, EEGCCT model significantly enhances generalization from limited data, effectively addressing a common limitation in EEG datasets. We validate and test our models using the open-source BCI Competition IV datasets 2a and 2b, employing a Leave-One-Subject-Out (LOSO) strategy to ensure subject-independent performance. Our findings demonstrate that EEGCCT not only outperforms conventional models like EEGNet in standard evaluations but also achieves better performance compared to other advanced models such as Conformer, Hybrid s-CViT, and Hybrid t-CViT, while utilizing fewer parameters and achieving an accuracy of 70.12%. Additionally, the paper presents a comprehensive ablation study that includes targeted data augmentation, hyperparameter optimization, and architectural improvements.
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Registered trials
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