2026

• Qamar, W.U.R., Abibullaev, B. Multi-scale EEG feature decoding with Swin Transformers for subject independent motor imagery BCIs. Sci Rep 16, 2503 (2026). https://doi.org/10.1038/s41598-025-32207-3
• A. Keutaeva, A. Zollanvari and B. Abibullaev, Visual Evoked Potentials in Neurofeedback: Advancing Cognitive Control in ADHD Through Therapeutic Gaming, In: Vinjamuri, R. (eds) "Bridging the Gap between Mind and Machine” Springer, Cham, Noveber, 2026

2025

• Qamar, WU Rehman, MH Lee, and B. Abibullaev, "Deep Learning in Intracranial EEG for Seizure Detection: Advances, Challenges, and Clinical Applications." Frontiers in Neuroscience 19: 1677898 (Link).
• A. Keutaeva, CJ. Nwachukwu, Z. Otarbay, M. Alaran and B. Abibullaev, Neurotechnology in Gaming: A Systematic Review of Visual Evoked Potential-Based Brain-Computer Interfaces, IEEEAccess, 2025, DOI: 10.1109/ACCESS.2025.3564328 (Link) .
• Kabdyshev, N., Umurbekov, I., Ziat, M., Topp, S., Duvernoy, B., Milroy, J., Kenzhebek, D., Abibullaev, B. and Kappassov, Z., 2025, September. HaptiComm-S20: Force-Feedback Characterization of Tactile Stimuli for Deafblind Communication. In Climbing and Walking Robots Conference (pp. 122-133). Cham: Springer Nature Switzerland.

2024

• A.Keutayeva, N. Fakhrutdinov and B. Abibullaev, “Compact Convolutional Transformer for Subject-Independent Motor Imagery EEG-Based BCIs,” Scientific Reports, 14, 25775 (2024). https://doi.org/10.1038/s41598-024-73755-4.
• A. Keutayeva and B. Abibullaev, “Data Constraints and Performance Optimization for Transformer-based Models in EEG-based Brain-Computer Interfaces: A Survey,” IEEE Access DOI: 10.1109/ACCESS.2024.3394696, 2024 [Link]
• A. Keutayeva, A. Zollanvari and B. Abibullaev, Evolving Trends and Future Prospects of Transformer Models in EEG-Based Motor-Imagery BCI Systems. In: Vinjamuri, R. (eds) Discovering the Frontiers of Human-Robot Interaction. Springer, Cham. https://doi.org/10.1007/978-3-031-66656-8_10

2023

• B. Abibullaev, A. Keutayeva, and A. Zollanvari, “Deep Learning in BCIs: A Comprehensive Review of Transformer Models, Advantages, Challenges, and Applications” IEEE Access, DOI: 10.1109/ACCESS.2023.3329678, 2023 [Link]
• A. Keutayeva and B. Abibullaev, "Subject-Independent Brain-Computer Interfaces: A Comparative Study of Attention Mechanism-Driven Deep Learning Models," 15th International Conference on IHCI-2023, Daegu, Korea, November 08 - 10, 2023
• I. Dolzhikova, B. Abibullaev, A. Zollanvari, A Jackknife-inspired deep learning approach to subject-independent classification of EEG, Pattern Recognition Letters, 2023, ISSN 0167-8655
• A. Keutayeva and B. Abibullaev, "Exploring the Potential of Attention Mechanism-Based Deep Learning for Robust Subject-Independent Motor-Imagery based BCIs," IEEE Access, doi: 10.1109/ACCESS.2023.3320561 [Link]
• V. Vladislav and B. Abibullaev. "Explainable Deep Learning for Brain-Computer Interfaces through Layerwise Relevance Propagation." In 2023 11th International Winter Conference on Brain-Computer Interface (BCI), pp. 1-5. IEEE, 2023.

2022

• B. Abibullaev, K. Kunanbayev and A. Zollanvari. Subject-Independent Classification of P300 Event-Related Potentials Using a Small Number of Training Subjects, IEEE Transactions on Human-Machine Systems, DOI: 10.1109/THMS.2022.3189576. [Link]
• I. Dolzhikova, B. Abibullaev, R. Sameni, and A. Zollanvari. "Subject-Independent Classification of Motor Imagery Tasks in EEG Using Multisubject Ensemble CNN." IEEE Access 10 (2022): 81355-81363. [Link]