Projects per year
Personal profile
Research interests
Our main research goal is to develop advanced machine learning algorithms to analyze and interpret neural signals such as EEG, EMG, and fNIRS. Our objective is to gain a better understanding of neural activity and its implications. This understanding is key to creating new, non-invasive methods for recording and interpreting brain activity, particularly in the improvement of brain-computer/machine interface technology.
We use a multidisciplinary approach combining robotics, computer science, neuroscience, and psychology. Our research could benefit assistive technologies, virtual reality, gaming interfaces, medical diagnostics, therapeutics, and cognitive enhancement tools. The multidisciplinary approach pushes technological boundaries and contributes to sectors that can benefit from enhanced brain understanding and interfacing.
For more information or if you are interested in joining our team, please visit us at brainu.notion.site
External positions
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Dive into the research topics where Berdakh Abibullaev is active. These topic labels come from the works of this person. Together they form a unique fingerprint.
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Collaborations and top research areas from the last five years
Recent external collaboration on country/territory level. Dive into details by clicking on the dots or
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Data-Driven Digital Twin (DT) Framework for Additive Manufacturing
Shehab, E., Ali, M. H., Hashmi, M., Abibullaev, B., Erkoyuncu, J. A. & Mahmoud, K.
1/1/22 → 12/31/24
Project: FDCRGP
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Exposure To Cooking Ultrafine Particles and Neurodegenerative Diseases: Clinical Exposure Studies and Computer Modeling
Shah, D., Crape, B., Zhumambayeva, S., Balanay, M., Abibullaev, B., Umezawa, M., Hopke, P. K., Torkmahalleh, M., Akmurzina, F., Kaumbekova, S., Yermek, M., Madiyarova, T., Naseri, M., Akhmetzhanova, Z., Kabibulina, D., Arstangaliyev, R. & Zhubatkanov, B.
1/1/22 → 12/31/24
Project: CRP
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Clinico-epidemiological assessment of COVID 19 infection in Kazakhstan
Colet, P., Sarria-Santamera, A., Abibullaev, B., Zollanvari, A., Azizan, A., Crape, B., Chan, C., Issanov, A., Stitt, N. & Viderman, D.
1/1/21 → 12/31/22
Project: FDCRGP
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Brain-Computer Interface to Exoskeleton System Design for Personalized Upper-Limb Rehabilitation after Stroke
Abibullaev, B., Shintemirov, A., Fazli, S., Zollanvari, A., Daribayev, Z. & Kaiyrzhanov, R.
1/1/21 → 12/31/23
Project: FDCRGP
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Neural-assisted optimization of human learning with multi-modal neuroimaging of EEG and NIRS
Fazli, S., Abibullaev, B., Müller, K. & Wallraven, C.
1/1/20 → 12/31/22
Project: FDCRGP
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Data Constraints and Performance Optimization for Transformer-Based Models in EEG-Based Brain-Computer Interfaces: A Survey
Keutayeva, A. & Abibullaev, B., 2024, In: IEEE Access. 12, p. 62628-62647 20 p.Research output: Contribution to journal › Article › peer-review
Open Access -
Subject-Independent Brain-Computer Interfaces: A Comparative Study of Attention Mechanism-Driven Deep Learning Models
Keutayeva, A. & Abibullaev, B., 2024, Intelligent Human Computer Interaction - 15th International Conference, IHCI 2023, Revised Selected Papers. Choi, B. J., Singh, D., Tiwary, U. S. & Chung, W-Y. (eds.). Springer Science and Business Media Deutschland GmbH, p. 245-254 10 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 14531 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
1 Citation (Scopus) -
A jackknifed-inspired deep learning approach to subject-independent classification of EEG
Dolzhikova, I., Abibullaev, B. & Zollanvari, A., Dec 2023, In: Pattern Recognition Letters. 176, p. 28-33 6 p.Research output: Contribution to journal › Article › peer-review
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Deep Learning in EEG-Based BCIs: A Comprehensive Review of Transformer Models, Advantages, Challenges, and Applications
Abibullaev, B., Keutayeva, A. & Zollanvari, A., 2023, In: IEEE Access. 11, p. 127271-127301 31 p.Research output: Contribution to journal › Article › peer-review
Open Access6 Citations (Scopus) -
Explainable Deep Learning for Brain-Computer Interfaces through Layerwise Relevance Propagation
Mun, V. & Abibullaev, B., 2023, 11th International Winter Conference on Brain-Computer Interface, BCI 2023. Institute of Electrical and Electronics Engineers Inc., (International Winter Conference on Brain-Computer Interface, BCI; vol. 2023-February).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
1 Citation (Scopus)