abibullaev-b 1

1. A Systematic Deep Learning Model Selection for P300-Based Brain-Computer Interfaces
2. Deep Learning in EEG-Based BCIs: A Comprehensive Review of Transformer Models, Advantages, Challenges, and Applications
3. Subject-Independent Classification of P300 Event-Related Potentials Using a Small Number of Training Subjects
4. A jackknifed-inspired deep learning approach to subject-independent classification of EEG
5. Subject-Independent Classification of Motor Imagery Tasks in EEG Using Multisubject Ensemble CNN
6. Compact convolutional transformer for subject-independent motor imagery EEG-based BCIs
7. Data Constraints and Performance Optimization for Transformer-Based Models in EEG-Based Brain-Computer Interfaces: A Survey
8. Exploring the Potential of Attention Mechanism-Based Deep Learning for Robust Subject-Independent Motor-Imagery Based BCIs
9. Neurotechnology in Gaming: A Systematic Review of Visual Evoked Potential-Based Brain-Computer Interfaces
10. Deep learning in intracranial EEG for seizure detection: advances, challenges, and clinical applications
11. Multi-scale EEG feature decoding with Swin Transformers for subject independent motor imagery BCIs
12. Bias correction for linear discriminant analysis
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