Dr. Berdakh Abibullaev earned his Ph.D. in Electronic Engineering from Yeungnam University in 2010, South Korea. He held research positions at Daegu-Gyeongbuk Institute of Science and Technology (DGIST) and in the Neurology Department of Samsung Medical Center, Seoul, Korea. He was also appointed as a research professor at Sungkyunkwan University, Seoul. In 2014, he was awarded NIH (National Institute of Health, USA) postdoctoral research fellowship to join a multi-institutional research project between the University of Houston Brain-Machine Interface Systems Team and various clinical institutions at Texas Medical Center in developing novel neural interfaces for neurorehabilitation in post-stroke patients. Currently, he is an assistant professor at the School of Science and Technology, Robotics and Mechatronics Department, Nazarbayev University.
Fig.1. A brain-machine interface system decodes different brain activity patterns produced by a user and translates into appropriate control and communication commands.
BMI systems have already been employed to control external devices, e.g. computer cursors  and robotic prostheses , using invasive methods. Moreover, in recent studies, BCIs have been used to control lower-body and upper-body exoskeletons for stroke and paraplegic recovery and rehabilitation via non-invasive approaches .
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 visual sensory stimulus associated with the selection of a specific character and thus allowing mental typing (see the demo at https://youtu.be/f3t-PzEq29A).
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 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 firstname.lastname@example.org.
- Wolpaw, Jonathan R., et al. “Brain–computer interfaces for communication and control.” Clinical neurophysiology 113.6 (2002): 767-791.
- 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.
- Hochberg, Leigh R., et al. “Reach and grasp by people with tetraplegia using a neurally controlled robotic arm.” Nature 485.7398 (2012): 372-375.
- 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 reports 2.2 (2014): 93-105.
Saduanov, B., Alizadeh, T., An, J., Abibullaev, B., 2018 2018 6th International Conference on Brain-Computer Interface, BCI 2018. Institute of Electrical and Electronics Engineers Inc., 2018-January, p. 1-4
Feature extraction and real-time recognition of hand motion intentions from EMGs via artificial neural networks
Oleinikov, A., Abibullaev, B., Shintemirov, A., Folgheraiter, M., 2018 2018 6th International Conference on Brain-Computer Interface, BCI 2018. Institute of Electrical and Electronics Engineers Inc., 2018-January, p. 1-5
Brain-Computer Interface Humanoid Pre-trained for Interaction with People
Saduanov, B., Tokmurzina, D., Alizadeh, T., Abibullaev, B., 2018 HRI 2018 - Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction. IEEE Computer Society, p. 229-230
Design and evaluation of a P300 visual brain-computer interface speller in cyrillic characters
Zhumadilova, A., Tokmurzina, D., Kuderbekov, A., Abibullaev, B., 2017 RO-MAN 2017 - 26th IEEE International Symposium on Robot and Human Interactive Communication. Institute of Electrical and Electronics Engineers Inc., 2017-January, p. 1006-1011
FNIRS motion artifact correction for overground walking using entropy based unbalanced optode decision and wavelet regression neural network
Lee, G., Jin, S., Lee, S., Abibullaev, B., An, J., 2017 MFI 2017 - 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems. Institute of Electrical and Electronics Engineers Inc., 2017-November, p. 186-193
Learning suite of kernel feature spaces enhances SMR-based EEG-BCI Classification
Abibullaev, B., 2017 5th International Winter Conference on Brain-Computer Interface, BCI 2017. Institute of Electrical and Electronics Engineers Inc., p. 55-59
Design and evaluation of action observation and motor imagery based BCIs using Near-Infrared Spectroscopy
Abibullaev, B., An, J., Lee, S., Moon, J., 2017 In : Measurement: Journal of the International Measurement Confederation. 98, p. 250-261
On robust classification of hemodynamic signals for BCIs via multiple kernel ν-SVM
Abibullaev, B., An, J., 2016 IROS 2016 - 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems. Institute of Electrical and Electronics Engineers Inc., 2016-November, p. 3063-3068
Design and optimization of an EEG-based brain machine interface (BMI) to an upper-limb exoskeleton for stroke survivors
Bhagat, N., Venkatakrishnan, A., Abibullaev, B., Artz, E., Yozbatiran, N., Blank, A., French, J., Karmonik, C., Grossman, R., O'Malley, M., Francisco, G., Contreras-Vidal, J., 2016 In : Frontiers in Neuroscience. 10, MAR,
A novel experimental and analytical approach to the multimodal neural decoding of intent during social interaction in freely-behaving human infants
Cruz-Garza, J., Hernandez, Z., Tse, T., Caducoy, E., Abibullaev, B., Contreras-Vidal, J., 2015 In : Journal of Visualized Experiments. 2015, 104,
EEG source imaging in partial epilepsy in comparison with presurgical evaluation and magnetoencephalography
Park, C., Seo, J., Kim, D., Abibullaev, B., Kwon, H., Lee, Y., Kim, M., An, K., Kim, K., Kim, J., Joo, E., Hong, S., 2015 In : Journal of Clinical Neurology (Korea). 11, 4, p. 319-330
Classification of brain hemodynamic signals arising from visual action observation tasks for brain-computer interfaces: A functional near-infrared spectroscopy study
Abibullaev, B., An, J., Jin, S., Moon, J., 2014 In : Measurement: Journal of the International Measurement Confederation. 49, 1, p. 320-328
Erratum: Classification of brain hemodynamic signals arising from visual action observation tasks for brain-computer interfaces: A functional near-infrared spectroscopy study (Measurement: Journal of the International Measurement Confederation (2014) 49 (320-328))
Abibullaev, B., An, J., Jin, S., Moon, J., 2014 In : Measurement: Journal of the International Measurement Confederation. 53, p. 297
Minimizing inter-subject variability in fNIRS-based brain-computer interfaces via multiple-kernel support vector learning
Abibullaev, B., An, J., Jin, S., Lee, S., Moon, J., 2013 In : Medical Engineering and Physics. 35, 12, p. 1811-1818
The beginning of neurohaptics: Controlling cognitive interaction via brain haptic interface
An, J., Lee, S., Jin, S., Abibullaev, B., Jang, G., Ahn, J., Lee, H., Moon, J., 2013 2013 International Winter Workshop on Brain-Computer Interface, BCI 2013. p. 103-106
Cortical activation pattern for grasping during observation, imagery, execution, FES, and observation-FES integrated BCI: An fNIRS pilot study
An, J., Jin, S., Lee, S., Jang, G., Abibullaev, B., Lee, H., Moon, J., 2013 Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. p. 6345-6348
Cortical activation pattern for grasping during observation, imagery, execution, FES, and observation-FES integrated BCI: an fNIRS pilot study
An, J., Jin, S., Lee, S., Jang, G., Abibullaev, B., Lee, H., Moon, J., 2013 In : Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference. 2013, p. 6345-6348
Classification of frontal cortex haemodynamic responses during cognitive tasks using wavelet transforms and machine learning algorithms
Abibullaev, B., An, J., 2012 In : Medical Engineering and Physics. 34, 10, p. 1394-1410
Decision support algorithm for diagnosis of ADHD using electroencephalograms
Abibullaev, B., An, J., 2012 In : Journal of Medical Systems. 36, 4, p. 2675-2688
A study on the BCI-Robot assisted stroke rehabilitation framework using brain hemodynamic signals
Abibullaev, B., An, J., Lee, S., Jin, S., Moon, J., 2012 2012 9th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2012. p. 500-504
Neural Network Classification of Brain Hemodynamic Responses from Four Mental Tasks
Abibullaev, B., An, J., Moon, J., 2011 In : International Journal of Optomechatronics. 5, 4, p. 340-359
A new QRS detection method using wavelets and artificial neural networks
Abibullaev, B., Seo, H., 2011 In : Journal of Medical Systems. 35, 4, p. 683-691
Seizure detection in temporal lobe epileptic EEGs using the best basis wavelet functions
Abibullaev, B., Kim, M., Seo, H., 2010 In : Journal of Medical Systems. 34, 4, p. 755-765
Epileptic spike detection using continuous wavelet transforms and artificial neural networks
Abibullaev, B., Seo, H., Kim, M., 2010 In : International Journal of Wavelets, Multiresolution and Information Processing. 8, 1, p. 33-48
Recognition of brain hemodynamic mental response for brain computer interface
Abibullaev, B., Kang, W., Lee, S., An, J., 2010 ICCAS 2010 - International Conference on Control, Automation and Systems. p. 2238-2243
Path planning algorithm using the values clustered by k-means
Kang, W., Lee, S., Abibullaev, B., Kim, J., An, J., 2010 Proceedings of the 15th International Symposium on Artificial Life and Robotics, AROB 15th'10. p. 959-962
Near-infrared spectroscopy in the analysis of functional brain activity during cognitive tasks
Abibullaev, B., Lee, S., Kang, W., An, J., Seo, H., 2010 Sensors 2010 IEEE. IEEE, p. 542-547
Near-infrared spectroscopy in the analysis of functional brain activity during cognitive tasks
Abibullaev, B., Lee, S., Kang, W., An, J., Seo, H., 2010 Proceedings of IEEE Sensors. p. 542-547
Functional near infrared spectroscopy based congitive task classification using support vector machines
Abibullaev, B., Kang, W., Lee, S., An, J., 2010 2010 5th International Symposium on Health Informatics and Bioinformatics, HIBIT 2010. p. 7-12
Classification of cardiac arrhythmias using biorthogonal wavelet preprocessing and SVM
Abibullaev, B., Kang, W., Lee, S., An, J., 2010 INC2010 - The International Conference on Networked Computing, Proceeding. p. 332-336
Analysis of attention deficit hyperactivity disorder in EEG using wavelet transform and self organizing maps
Lee, S., Abibullaev, B., Kang, W., Shin, Y., An, J., 2010 ICCAS 2010 - International Conference on Control, Automation and Systems. p. 2439-2442
Epileptic seizures detection using continuous time wavelet based artificial neural networks
Berdakh, A., Don, S., 2009 ITNG 2009 - 6th International Conference on Information Technology: New Generations. p. 1456-1461
A wavelet based method for detecting and localizing epileptic neural spikes in EEG
Abibullaev, B., Seo, H., Kang, W., An, J., 2009 ACM International Conference Proceeding Series. 403, p. 702-707
Analysis of brain function and classification of sleep stage EEG using daubechies wavelet
Kim, M., Cho, Y., Berdakh, A., Seo, H., 2008 In : Sensors and Materials. 20, 1, p. 1-14