Berdakh Abibullaev

School of Engineering and Digital Sciences, Robotics
Assistant Professor


Dr. Berdakh Abibullaev received his M.Sc. and Ph.D. degrees in electronic engineering from Yeungnam University, South Korea in 2006 and 2010 under the Korean Government Scholarship program. He held research scientist positions at Daegu-Gyeongbuk Institute of Science and Technology  (2010-2013) and Samsung Medical Center, South Korea (2013-2014). He was also appointed as a research professor at Sungkyunkwan University, Seoul. In 2014,  he received NIH (National Institute of Health,  USA), a postdoctoral research fellowship to join a research project between the University of Houston Brain-Machine Interface Systems Team and a Texas Medical Center in developing neural interfaces for rehabilitation in post-stroke patients.  Currently, he is an Assistant Professor in the Department of Robotics and Mechatronics,  Nazarbayev University,  Kazakhstan. 

His current research focuses on signal processing and machine learning algorithms for the inference problems of Brain-Computer Interfaces, and brain data analytics. 
He is an IEEE Senior Member, and also serves as associate editor of IEEE Access and PeerJ Computer Science. 
Research Area
The Brain-Computer/Machine Interfaces (BCI/BMI) research aims to restore or substitute lost motor function in patients with neurological conditions such as stroke, spinal cord injury, amyotrophic lateral sclerosis, or in patients with amputated limbs. This technology, which is also known as a thought-translation device, is based on building a direct communication and control channel between humans and an external device without involving any peripheral and muscular activity [1] (see Fig. 1).

Fig.1. A brain-machine interface system decodes different brain activity patterns produced by a user and translates them into appropriate control and communication commands.

 BMI systems have already been employed to control external devices, e.g. computer cursors [2] and robotic prostheses [3], using invasive methods.  Moreover, in recent studies, BMIs have been used to control lower-body and upper-body exoskeletons for stroke and paraplegic recovery and rehabilitation via non-invasive approaches [4].

Our research at NU focuses on the development and cross-validation of new neurotechnologies in Kazakhstan to improve the quality of life for disabled people, at the interface between engineering, robotics, and neuroscience.  Currently, we are working on the following research topics:

  • to enable communication capability between brains and computers,
  • to develop neural interfaces to restore human motor functions after stroke.
  • to develop brain-actuated assistive robotic systems for disabled persons

Design and optimization of a Brain-Computer Interface speller in the Kazakh language.

Recently, we have finished the design and optimization of the Kazakh language-based BCI speller on healthy subjects and plan to conduct clinical trials with patients suffering amyotrophic lateral sclerosis (ALS). This research will be conducted in collaboration with the National Center for Neurosurgery in Astana, and it should enable patients to communicate with their relatives, and caregivers via our BCI technology.  

Fig.2. The Kazakh language speller. The BCI decodes electrical brain activities time-locked to a visual sensory stimulus associated with the selection of a specific character and thus allowing mental typing (see the demo at

Opportunity for students

The undergraduate and graduate students at NU  have an opportunity to actively participate in the projects related to BMI/BCI systems with the investigator. Our lab is equipped with a high-density 64-channel scalp electroencephalography system (Guger Technologies, Austria) with active electrode caps.  The research mentioned is wide enough to create many strong thesis topics for any students who are interested in the research to build novel neural interfaces for different applications. If interested please contact me at 

  1. Wolpaw, Jonathan R., et al. “Brain-computer interfaces for communication and control.” Clinical Neurophysiology 113.6 (2002): 767-791.
  2. Kim, Sung-Phil, et al. “Point-and-click cursor control with an intracortical neural interface system by humans with tetraplegia.” IEEE Transactions on Neural Systems and Rehabilitation Engineering 19.2 (2011): 193-203.
  3. Hochberg, Leigh R., et al. “Reach and grasp by people with tetraplegia using a neurally controlled robotic arm.” Nature 485.7398 (2012): 372-375.
  4. Venkatakrishnan, Anusha, Gerard E. Francisco, and Jose L. Contreras-Vidal. “Applications of brain-machine interface systems in stroke recovery and rehabilitation.” Current physical medicine and rehabilitation report 2.2 (2014): 93-105.