Berdakh Abibullaev, PhD

Berdakh Abibullaev, PhD

Machine Learning Engineer, Data Scientist, and Researcher

Publications

70+ peer-reviewed publications in machine learning, neural signal processing, and brain-computer interfaces. For citation metrics, see Google Scholar (h-index 21) and Scopus.

Journal Articles

  1. W. U. R. Qamar, B. Abibullaev, Multi-scale EEG feature decoding with Swin Transformers for subject independent motor imagery BCIs, Scientific Reports, vol. 16, no. 1, pp. 2503, 2026; DOI: (link)

  2. W. U. R. Qamar, M. Lee, B. Abibullaev, Deep Learning in Intracranial EEG for Seizure Detection: Advances, Challenges, and Clinical Applications, Frontiers in Neuroscience, vol. 19, no. 1677898, 2025; DOI: (link)

  3. A. Keutayeva, C. J. Nwachukwu, M. Alaran, Z. Otarbay, B. Abibullaev, Neurotechnology in Gaming: A Systematic Review of Visual Evoked Potential-Based Brain-Computer Interfaces, IEEE Access, 2025; DOI: (link)

  4. A. Keutayeva, B. Abibullaev, Compact convolutional transformer for subject-independent motor imagery EEG-based BCIs, Scientific Reports, vol. 14, no. 1, pp. 25775, 2024; DOI: (link)

  5. A. Keutayeva, B. Abibullaev, Data constraints and performance optimization for transformer-based models in eeg-based brain-computer interfaces: A survey, IEEE Access, vol. 12, pp. 62628–62647, 2024; DOI: (link)

  6. I. Dolzhikova, B. Abibullaev, A. Zollanvari, A Jackknife-Inspired Deep Learning Approach to Subject-Independent Classification of EEG, Pattern Recognition Letters, vol. 176, pp. 28–33, 2023; DOI: (link)

  7. A. Keutayeva, B. Abibullaev, Exploring the potential of attention mechanism-based deep learning for robust subject-independent motor-imagery based BCIs, IEEE Access, vol. 11, pp. 107562–107580, 2023; DOI: (link)

  8. B. Abibullaev, A. Keutayeva, A. Zollanvari, Deep learning in EEG-based BCIs: A comprehensive review of transformer models, advantages, challenges, and applications, IEEE Access, vol. 11, pp. 127271–127301, 2023; DOI: (link)

  9. I. Dolzhikova, B. Abibullaev, R. Sameni, A. Zollanvari, Subject-independent classification of motor imagery tasks in EEG using multisubject ensemble CNN, IEEE Access, vol. 10, pp. 81355–81363, 2022; DOI: (link)

  10. B. Abibullaev, K. Kunanbayev, A. Zollanvari, Subject-independent classification of P300 event-related potentials using a small number of training subjects, IEEE Transactions on Human-Machine Systems, vol. 52, no. 5, pp. 843–854, 2022; DOI: (link)

  11. A. Zollanvari, B. Abibullaev, Bias correction for linear discriminant analysis, Pattern Recognition Letters, vol. 151, pp. 41–47, 2021; DOI: (link)

  12. B. Abibullaev, A. Zollanvari, A systematic deep learning model selection for P300-based brain–computer interfaces, IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 52, no. 5, pp. 2744–2756, 2022; DOI: (link)

  13. B. Abibullaev, I. Dolzhikova, A. Zollanvari, A Brute-force CNN Model Selection for Accurate Classification of Sensorimotor Rhythms in BCIs, IEEE Access, vol. 8, pp. 101014–101023, 2020; DOI: (link)

  14. A. Zollanvari, M. Abdirash, A. Dadlani, B. Abibullaev, Asymptotically Bias-Corrected Regularized Linear Discriminant Analysis for Cost-Sensitive Binary Classification, IEEE Signal Processing Letters, vol. 26, no. 5, pp. 724–728, 2019; DOI: (link)

  15. B. Abibullaev, A. Zollanvari, B. Saduanov, T. Alizadeh, Design and Optimization of a BCI-Driven Telepresence Robot Through Programming by Demonstration, IEEE Access, vol. 7, pp. 111625–111636, 2019; DOI: (link)

  16. B. Abibullaev, A. Zollanvari, Learning Discriminative Spatiospectral Features of ERPs for Accurate Brain-Computer Interfaces, IEEE Journal of Biomedical and Health Informatics, vol. 23, no. 5, pp. 2009–2020, 2019; DOI: (link)

  17. B. Abibullaev, J. An, S. H. Lee, J. I. Moon, Design and evaluation of action observation and motor imagery based BCIs using near-infrared spectroscopy, Measurement, vol. 98, pp. 250–261, 2017; DOI: (link)

  18. N. Bhagat, A. Venkatakrishnan, B. Abibullaev, E. Artz, N. Yozbatiran, A. Blank, J. French, C. Karmonik, R. G. Grossman, M. K. O’Malley, G. E. Francisco, J. L. Contreras-Vidal, Design and optimization of an EEG-based brain machine interface (BMI) to an upper-limb exoskeleton for stroke survivors, Frontiers in Neuroscience, vol. 10, pp. 1–17, 2016; DOI: (link)

  19. J. G. Cruz-Garza, Z. R. Hernandez, T. Tse, E. Caducoy, B. Abibullaev, J. L. Contreras-Vidal, A novel experimental and analytical approach to the multimodal neural decoding of intent during social interaction in freely-behaving human infants, Journal of Visualized Experiments: JoVE, no. 104, pp. 53406, 2015; DOI: (link)

  20. C. Park, J. H. Seo, B. Abibullaev, D. Kim, et al., EEG source imaging in partial epilepsy in comparison with presurgical evaluation and magnetoencephalography, Journal of Clinical Neurology, vol. 11, no. 4, pp. 319–330, 2015; DOI: (link)

  21. B. Abibullaev, J. An, S. H. Jin, J. I. Moon, Classification of brain hemodynamic signals arising from visual action observation tasks for brain–computer interfaces: a functional near-infrared spectroscopy study, Measurement, vol. 49, pp. 320–328, 2014; DOI: (link)

  22. B. Abibullaev, J. An, S. H. Jin, S. H. Lee, J. I. Moon, Minimizing inter-subject variability in fNIRS-based brain–computer interfaces via multiple-kernel support vector learning, Medical Engineering & Physics, vol. 35, no. 12, pp. 1811–1818, 2013; DOI: (link)

  23. B. Abibullaev, J. An, Decision support algorithm for diagnosis of ADHD using electroencephalograms, Journal of Medical Systems, vol. 36, no. 4, pp. 2675–2688, 2012; DOI: (link)

  24. B. Abibullaev, J. An, Classification of frontal cortex haemodynamic responses during cognitive tasks using wavelet transforms and machine learning algorithms, Medical Engineering & Physics, vol. 34, no. 10, pp. 1394–1410, 2012; DOI: (link)

  25. B. Abibullaev, J. An, J. I. Moon, Neural network classification of brain hemodynamic responses from four mental tasks, International Journal of Optomechatronics, vol. 5, no. 4, pp. 340–359, 2011; DOI: (link)

  26. B. Abibullaev, H. D. Seo, A new QRS detection method using wavelets and artificial neural networks, Journal of Medical Systems, vol. 35, no. 4, pp. 683–691, 2011; DOI: (link)

  27. B. Abibullaev, M. S. Kim, H. D. Seo, Seizure detection in temporal lobe epileptic EEGs using the best basis wavelet functions, Journal of Medical Systems, vol. 34, no. 4, pp. 755–765, 2010; DOI: (link)

  28. B. Abibullaev, H. D. Seo, M. S. Kim, Epileptic spike detection using continuous wavelet transforms and artificial neural networks, International Journal of Wavelets, Multiresolution and Information Processing, vol. 8, no. 01, pp. 33–48, 2010; DOI: (link)

  29. M. S. Kim, Y. C. Cho, B. Abibullaev, H. D. Seo, Analysis of brain function and classification of sleep stage EEG using Daubechies wavelet, Sensors and Materials, vol. 20, no. 1, pp. 1–15, 2008; DOI: (link)

Book Chapters

  1. A. Keutayeva, A. Zollanvari, B. Abibullaev, Visual Evoked Potentials in Neurofeedback: Advancing Cognitive Control in ADHD Through Therapeutic Gaming, Bridging the Gap between Mind and Machine: Exploring the Future of Human-AI-Neurotechnology Integration, pp. 131–154, 2025; DOI: (link)

  2. A. Keutayeva, A. Zollanvari, B. Abibullaev,Evolving Trends and Future Prospects of Transformer Models in EEG-Based Motor-Imagery BCI Systems, Discovering the Frontiers of Human-Robot Interaction: Insights and Innovations in Collaboration, Communication, and Control, pp. 233, 2024; DOI: (link)

Conference Papers

  1. N. Kabdyshev, I. Umurbekov, M. Ziat, S. Topp, B. Duvernoy, J. Milroy, D. Kenzhebek, B. Abibullaev, Z. Kappassov. HaptiComm-S20: Force-Feedback Characterization of Tactile Stimuli for Deafblind Communication. Climbing and Walking Robots Conference, pp. 122–133, 2025.

  2. V. Mun, B. Abibullaev. Explainable deep learning for brain-computer interfaces through layerwise relevance propagation. 2023 11th International Winter Conference on Brain-Computer Interface (BCI), pp. 1–5, 2023.

  3. A. Keutayeva, B. Abibullaev. Subject-Independent Brain-Computer Interfaces: A Comparative Study of Attention Mechanism-Driven Deep Learning Models. International Conference on Intelligent Human Computer Interaction, pp. 245–254, 2023.

  4. I. Dolzhikova, B. Abibullaev, A. Zollanvari. An ensemble of convolutional neural networks for zero-calibration ERP-based BCIs. 2022 10th International Winter Conference on Brain-Computer Interface (BCI), pp. 1–4, 2022.

  5. Y. Kainolda, B. Abibullaev, R. Sameni, A. Zollanvari. Is Riemannian geometry better than Euclidean in averaging covariance matrices for CSP-based subject-independent classification of motor imagery? 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 910–914, 2021.

  6. S. Abilkassov, M. Kairgaliyev, B. Zhakanov, B. Abibullaev. A System For Drivers’ Cognitive Load Estimation Based On Deep Convolutional Neural Networks and Facial Feature Analysis. 2021 22nd IEEE International Conference on Industrial Technology (ICIT), vol. 1, pp. 994–1000, 2021.

  7. M. Nurpeiissov, B. Abibullaev, T. Alizadeh. A Novel Human-Robot Interaction Framework Based on Telegram and Programming by Demonstration. International Conference on Robot Intelligence Technology and Applications, pp. 498–507, 2021.

  8. K. Kunanbayev, D. Azhigulov, B. Abibullaev, A. Zollanvari. Deep Transfer Learning for Subject-Independent ERP-based BCIs. 2021 9th International Winter Conference on Brain-Computer Interface (BCI), pp. 1–3, 2021.

  9. K. Kunanbayev, B. Abibullaev, A. Zollanvari. Data augmentation for p300-based brain-computer interfaces using generative adversarial networks. 2021 9th International Winter Conference on Brain-Computer Interface (BCI), pp. 1–7, 2021.

  10. I. Dolzhikova, B. Abibullaev, R. Sameni, A. Zollanvari. An ensemble CNN for subject-independent classification of motor imagery-based EEG. 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 319–324, 2021.

  11. B. Saduanov, D. Tokmurzina, K. Kunanbayev, B. Abibullaev. Design and optimization of a real-time asynchronous BCI control strategy for robotic manipulator assistance. 2020 8th International Winter Conference on Brain-Computer Interface (BCI), pp. 1–5, 2020.

  12. A. Oleinikov, B. Abibullaev, M. Folgheraiter. On the classification of electromyography signals to control a four degree-of-freedom prosthetic device. 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 686–689, 2020.

  13. A. Tuleuov, B. Abibullaev. Deep learning models for subject-independent ERP-based brain-computer interfaces. 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER), pp. 945–948, 2019.

  14. B. Abibullaev, Y. Orazayev, A. Zollanvari. Novel spatiospectral features of ERPs enhances brain-computer interfaces. 2019 7th International Winter Conference on Brain-Computer Interface (BCI), pp. 1–4, 2019.

  15. G. Lee, S. H. Jin, S. T. Yang, J. An, B. Abibullaev. Cross-correlation between HbO and HbR as an effective feature of motion artifact in fNIRS signal. 2018 6th International Conference on Brain-Computer Interface (BCI), pp. 1–3, 2018.

  16. B. Saduanov, T. Alizadeh, J. An, B. Abibullaev. Trained by demonstration humanoid robot controlled via a BCI system for telepresence. 2018 6th International Conference on Brain-Computer Interface (BCI), pp. 1–4, 2018.

  17. B. Saduanov, D. Tokmurzina, T. Alizadeh, B. Abibullaev. Brain-Computer Interface Humanoid Pre-trained for Interaction with People. Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, pp. 229–230, 2018.

  18. A. Oleinikov, B. Abibullaev, A. Shintemirov, M. Folgheraiter. Feature extraction and real-time recognition of hand motion intentions from EMGs via artificial neural networks. 2018 6th International Conference on Brain-Computer Interface (BCI), pp. 1–5, 2018.

  19. G. Lee, S. H. Jin, S. H. Lee, B. Abibullaev, J. An. fNIRS motion artifact correction for overground walking using entropy based unbalanced optode decision and wavelet regression neural network. 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), pp. 186–193, 2017.

  20. D. Nurseitov, A. Serekov, A. Shintemirov, B. Abibullaev. Design and evaluation of a P300-ERP based BCI system for real-time control of a mobile robot. 2017 5th International Winter Conference on Brain-Computer Interface (BCI), pp. 115–120, 2017.

  21. A. Zhumadilova, D. Tokmurzina, A. Kuderbekov, B. Abibullaev. Design and evaluation of a P300 visual brain-computer interface speller in cyrillic characters. 2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 1006–1011, 2017.

  22. B. Abibullaev. Learning suite of kernel feature spaces enhances SMR-based EEG-BCI classification. 2017 5th international winter conference on brain-computer interface (BCI), pp. 55–59, 2017.

  23. B. Abibullaev, J. An. On robust classification of hemodynamic signals for BCIs via multiple kernel ν-SVM. 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3063–3068, 2016.

  24. Z. R. Hernandez, J. G. Cruz-Garza, T. Tse, E. Caducoy, B. Abibullaev, J. L. Contreras-Vidal. Supervised Classification of Intended Behaviors Using Electroencephalography (EEG) from Freely-Behaving Infants: Early findings. 12th Annual Theoretical and Computational Neuroscience Conference; Houston, TX, USA, 2015.

  25. J. G. Cruz-Garza, Z. R. Hernandez, M. Megjhani, B. Abibullaev, T. Tse, E. Caducoy, and J. L. Contreras-Vidal. Neural development of social cognition in the first two years of life - Early findings. In Society for Neuroscience; Chicago, USA, 2015.

  26. A. J. Arenas-Castellanos, Z. R. Hernandez, J. G. Cruz-Garza, M. Megjhani, B. Abibullaev, Sri. R. P. Maddi, T. Tse, C. Armstrong, W. Long, J. L. Contreras-Vidal. A developmental Analysis of Behaviors Related to the Mirror Neuron System in 6-24 Months Infants. In Ninth Biennial Meeting of the Cognitive Development Society Ohio, USA, 2015.

  27. C. H. Park, D. Kim, B. Abibullaev, H. Kwon, E. Y. Joo, Y. H. Lee, S. B. Hong. The evaluation of accuracy and clinical usefulness of 3D EEG Source Localization Analysis. Korean Epilepsy Congress, Seoul, South Korea, 2014.

  28. N. A. Bhagat, A. Venkatakrishnan, B. Abibullaev, E. J. Artz, A. A. Blank, J. A. French, N. Yozbatiran, and R. G. Grossman, M. K. O’Malley, J. L. Contreras-Vidal, G. E. Francisco. BMI Control of a Therapeutic Exoskeleton to Facilitate Personalized Robotic Rehabilitation of the Upper Limb. In National Robotics Initiative (NRI) PI Meeting, Westin Arlington, United States, 2014.

  29. S. H. Lee, J. An, G. Jang, S. H. Jin, B. Abibullaev, H. Lee, J. I. Moon. Neural activity during observation, imagery, and execution of eating: An fNIRS pilot study. In the 19th Annual Meeting of the Organization for Human Brain Mapping. Seattle, WA, USA, 2013.

  30. J. An, S. H. Lee, S. H. Jin, B. Abibullaev, G. Jang, J. Ahn, H. Lee, J. I. Moon. The beginning of neurohaptics: Controlling cognitive interaction via brain haptic interface. 2013 International Winter Workshop on Brain-Computer Interface (BCI), pp. 103–106, 2013.

  31. J. An, S. H. Jin, S. H. Lee, G. Jang, B. Abibullaev, H. Lee, J. I. Moon. Cortical activation pattern for grasping during observation, imagery, execution, FES, and observation-FES integrated BCI: An fNIRS pilot study. 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 6345–6348, 2013.

  32. B. Abibullaev, J. An, S. H. Lee, S. H. Jin, J. I. Moon. A study on the BCI-Robot assisted stroke rehabilitation framework using brain hemodynamic signals. 2012 9th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), pp. 500–504, 2012.

  33. W. S. Kang, S. H. Lee, B. Abibullaev, J. W. Kim, J. An, et al. Path planning algorithm using the values clustered by k-means. 15th International Symposium on Artificial Life and Robotics, AROB’10, pp. 959–962, 2010.

  34. S. H. Lee, B. Abibullaev, W. S. Kang, Y. Shin, J. An. Analysis of attention deficit hyperactivity disorder in EEG using wavelet transform and self organizing maps. ICCAS 2010, pp. 2439–2442, 2010.

  35. B. Abibullaev, W. S. Kang, S. H. Lee, J. An. Recognition of brain hemodynamic mental response for brain computer interface. ICCAS 2010, pp. 2238–2243, 2010.

  36. B. Abibullaev, W. S. Kang, S. H. Lee, J. An. Functional near infrared spectroscopy based cognitive task classification using support vector machines. 2010 5th International Symposium on Health Informatics and Bioinformatics, pp. 7–12, 2010.

  37. B. Abibullaev, W. S. Kang, S. H. Lee, J. An. Classification of cardiac arrhythmias using biorthogonal wavelet preprocessing and SVM. INC2010: 6th International Conference on Networked Computing, pp. 1–5, 2010.

  38. B. Abibullaev, S. H. Lee, W. S. Kang, J. An, H. D. Seo. Near-infrared spectroscopy in the analysis of functional brain activity during cognitive tasks. SENSORS, 2010 IEEE, pp. 542–547, 2010.

  39. W. S. Kang, B. Abibullaev, S. Lee, J. An. A Study on Brain Activation during playing a computer game using a fNIRS. Annual Conference of KIPS, pp. 407–408, 2009.

  40. B. Abibullaev, S. H. Don. Epileptic seizures detection using continuous time wavelet based artificial neural networks. 2009 Sixth International Conference on Information Technology: New Generations, pp. 1456–1461, 2009.

Patents

  • J. An, S.H. Jin, S.H. Lee, J.I. Moon, B. Abibullaev, J.H. Ahn and G.H. Jang. Rehabilitation Training System and Method. : 9,081,890, Washington, DC: United States. Patent and Trademark Office, 2015 [Link].
  • J. An, S.H. Jin, S.H. Lee, J.I. Moon, B. Abibullaev, J.H. Ahn and G.H. Jang. Self-directed Rehabilitation Training Method Combining Brain Signals and Functional Electrostimulation. : 17077057, 14049302, 09-OCT-2013, United States, [Link].
  • J. An, S.H. Jin, S.H. Lee, B. Abibullaev, J.I. Moon , J.H. Ahn and G.H. Jang. Combining Brain Signals and Functional Electrostimulation Self-Directed Rehabilitation Method, Patent Registration Number: 101501524000, 05.03.2015. South Korea, [Link].