Berdakh Abibullaev, PhD

Assistant Professor

Accepting PhD Students

PhD projects

Brain-Machine Interfaces for Neural Rehabilitation after Stroke The brain-machine interface (BMI) technology aims to restore motor function disability in patients after stroke. This research work will contribute to the development and cross-validation of the BMI systems in Kazakhstan to improve the quality of life for disabled and motor-impaired people. It will focus on areas at the interface between engineering, robotics, neuroscience, and medicine. A physical human-robot interface will be designed and augmented with a non-invasive BMI to provide neural rehabilitation therapy for patients after stroke across a broad spectrum of impairment severity in the rehabilitation tasks. One of the key contributions of this proposed work will include: devising advanced electroencephalogram (EEG) interface methods (e.g. signal processing and machine learning algorithms) to stroke patients and developing a BMI for the control of the therapeutic robot that will improve upper and lower limb motor function, as well as to conduct clinical trials in collaboration with the local rehabilitation centers. Annually, over 49 thousand people suffer a stroke in Kazakhstan, 80% of them became wholly or partly disabled after the strokes (, and this research project will have a substantial impact in the engineering of novel tools for efficient stroke rehabilitation of patients locally.

  • 300 Citations
  • 9 h-Index
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Personal profile

Personal profile

Dr. Berdakh Abibullaev received his M.Sc. and Ph.D. degrees in electronic engineering from Yeungnam University, South Korea in 2006, and 2010,  respectively. He held research scientist positions at Daegu-Gyeongbuk Institute of Science and Technology (2010-2013) and at Samsung Medical Center,  Seoul,  South Korea (2013-2014). In 2014,  he received the National Institute of Health postdoctoral research fellowship II to join a multi-institutional research project between the University of Houston Brain-Machine Interface Systems Team and Texas Medical Center in developing neural interfaces for rehabilitation in post-stroke patients. He is currently an Assistant Professor at Robotics Department, Nazarbayev University,  Kazakhstan. His research focuses on developing robust Brain-Machine Interfaces for people with severe motor impairments.

External positions

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Hemodynamics Engineering & Materials Science
Brain-Computer Interfaces Medicine & Life Sciences
Electroencephalography Engineering & Materials Science
Neural networks Engineering & Materials Science
Classifiers Engineering & Materials Science

Network Recent external collaboration on country level. Dive into details by clicking on the dots.

Projects 2020 2022

Intelligent systems
Learning systems
Information use

Research Output 2008 2019

  • 300 Citations
  • 9 h-Index
  • 20 Conference contribution
  • 16 Article
  • 1 Chapter
Binary Classification
Discriminant analysis
Discriminant Analysis
Gaussian distribution

Design and Optimization of a BCI-Driven Telepresence Robot Through Programming by Demonstration

Abibullaev, B., Zollanvari, A., Saduanov, B. & Alizadeh, T., Aug 5 2019, In : IEEE Access. 7, p. 111625 111636 p.

Research output: Contribution to journalArticle

Open Access
Brain computer interface

Novel Spatiospectral Features of ERPs Enhances Brain-Computer Interfaces

Orazayev, Y., Zollanvari, A. & Abibullaev, B., Feb 1 2019, 7th International Winter Conference on Brain-Computer Interface, BCI 2019. Institute of Electrical and Electronics Engineers Inc., 8737344. (7th International Winter Conference on Brain-Computer Interface, BCI 2019).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Brain-Computer Interfaces
Brain computer interface
Evoked Potentials

Brain-Computer Interface Humanoid Pre-trained for Interaction with People

Saduanov, B., Tokmurzina, D., Alizadeh, T. & Abibullaev, B., Mar 1 2018, HRI 2018 - Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction. IEEE Computer Society, p. 229-230 2 p.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Brain computer interface
5 Citations (Scopus)

Feature extraction and real-time recognition of hand motion intentions from EMGs via artificial neural networks

Oleinikov, A., Abibullaev, B., Shintemirov, A. & Folgheraiter, M., Mar 9 2018, 2018 6th International Conference on Brain-Computer Interface, BCI 2018. Institute of Electrical and Electronics Engineers Inc., Vol. 2018-January. p. 1-5 5 p.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Feature extraction
Neural networks