Berdakh Abibullaev, PhD

Assistant Professor

Accepting PhD Students

PhD projects

1. 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 (http://en.tengrinews.kz/health/-50-000-Kazakhstan-citizens-suffer-strokes-annually-9657/), and this research project will have a substantial impact in the engineering of novel tools for efficient stroke rehabilitation of patients locally. 2. Design and Optimization of a P300 Visual Brain-Computer Interface Speller for patients in Kazakhstan. Approximately half a million people living with Amyotrophic Lateral Sclerosis (ALS), a neurological disease that affects neurons responsible for voluntary movements. Patients rapidly lose the ability to move their arms, legs, and face muscles. Gradually, ALS patients become unable to communicate. However, the brain of an ALS patient is fully functional; this is an opportunity that provides a gateway to developing assistive devices for communication. The only means of communication for those patients are using a Brain-Computer Interface (BCI). The BCI speller is a system that enables persons unable to communicate naturally due to neuromuscular diseases to communicate with the external world. It acquires brain signals in response to visual stimuli the person is shown to on the screen and then analyzes in real-time to predict the desired symbol. To date, most BCI design paradigms have been focused on the development of a speller to communicate English or Latin-based languages. Due to a lack of BCI spellers for patients speaking the Kazakh language, this study will design and optimize a new BCI speller in collaboration with the local medical centers to enable thousands of ALS patients to communicate the Kazakh language. Besides, the BCI speller could be potentially useful for people with communication problems: persons afflicted with brain or spinal cord injuries, multiple sclerosis, muscular dystrophies, and cerebral palsy. 3. Feature Representation Learning of High-Frequency Oscillations strongly relevant to Ictogenesis Epilepsy affects approximately 1% of the population, and about 30~40% of them fail to control seizures with medications. For these patients with medically refractory epilepsy, epilepsy surgery is the best treatment option currently available. The success of epilepsy surgery largely depends on the precise localization of the epileptogenic region, brain areas that are capable of generating spontaneous seizures now or in the future, and that should be surgically removed or disconnected. The current clinical practice encompasses the identification of the seizure onset zone (SOZ) and extended epileptic area (or irritative zone) by multiple days of intracranial EEG recordings and the surgical resection of these regions. However, these invasive procedures do not always warrant favorable surgical outcome: for its worse, the seizure freedom rate after surgery declines with time. Among many candidates of an electrophysiological biomarker of an epileptic brain, high-frequency oscillations (HFO) are becoming the most promising ones. This study aims to investigate how different types of HFOs are related to Ictogenesis through developing a mathematical framework such as feature representation learning algorithms. And, to manifest the existence of novel predictive/distinct features that are hidden but essential biomarkers useful for accurate delineation of SOZ. The proposed machine learning-based extraction of HFO signatures will help physicians 1) to indicate accurate localization of epileptogenic zone in pre-surgical evaluations and, 2) to better understand which epileptic HFO discharges are highly relevant to ictal onset zone and lead to a better postsurgical outcome.

  • 292 Citations
  • 9 h-Index
20082019
If you made any changes in Pure these will be visible here soon.

Research Output 2008 2019

  • 292 Citations
  • 9 h-Index
  • 20 Conference contribution
  • 16 Article
  • 1 Chapter
Filter
Article
2019
Binary Classification
Discriminant analysis
Discriminant Analysis
Misclassification
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
Demonstrations
Robots
Decoding
Brain
2017
4 Citations (Scopus)

Design and evaluation of action observation and motor imagery based BCIs using Near-Infrared Spectroscopy

Abibullaev, B., An, J., Lee, S. H. & Moon, J. I., Feb 1 2017, In : Measurement: Journal of the International Measurement Confederation. 98, p. 250-261 12 p.

Research output: Contribution to journalArticle

Near-infrared Spectroscopy
Brain computer interface
Near infrared spectroscopy
imagery
brain
2016
43 Citations (Scopus)

Design and optimization of an EEG-based brain machine interface (BMI) to an upper-limb exoskeleton for stroke survivors

Bhagat, N. A., Venkatakrishnan, A., Abibullaev, B., Artz, E. J., Yozbatiran, N., Blank, A. A., French, J., Karmonik, C., Grossman, R. G., O'Malley, M. K., Francisco, G. E. & Contreras-Vidal, J. L., Mar 31 2016, In : Frontiers in Neuroscience. 10, MAR, 122.

Research output: Contribution to journalArticle

Brain-Computer Interfaces
Upper Extremity
Survivors
Electroencephalography
Stroke
2015
2 Citations (Scopus)

A novel experimental and analytical approach to the multimodal neural decoding of intent during social interaction in freely-behaving human infants

Cruz-Garza, J. G., Hernandez, Z. R., Tse, T., Caducoy, E., Abibullaev, B. & Contreras-Vidal, J. L., Oct 4 2015, In : Journal of Visualized Experiments. 2015, 104, e53406.

Research output: Contribution to journalArticle

Interpersonal Relations
Decoding
Brain
Neural networks
Video recording
5 Citations (Scopus)

EEG source imaging in partial epilepsy in comparison with presurgical evaluation and magnetoencephalography

Park, C. J., Seo, J. H., Kim, D., Abibullaev, B., Kwon, H., Lee, Y. H., Kim, M. Y., An, K. M., Kim, K., Kim, J. S., Joo, E. Y. & Hong, S. B., Oct 1 2015, In : Journal of Clinical Neurology (Korea). 11, 4, p. 319-330 12 p.

Research output: Contribution to journalArticle

Magnetoencephalography
Partial Epilepsy
Electroencephalography
Scalp
Electrodes
2014
4 Citations (Scopus)
Near-infrared Spectroscopy
Brain computer interface
hemodynamics
Near infrared spectroscopy
Hemodynamics
2013
15 Citations (Scopus)

Minimizing inter-subject variability in fNIRS-based brain-computer interfaces via multiple-kernel support vector learning

Abibullaev, B., An, J., Jin, S. H., Lee, S. H. & Moon, J. I., Dec 2013, In : Medical Engineering and Physics. 35, 12, p. 1811-1818 8 p.

Research output: Contribution to journalArticle

Brain-Computer Interfaces
Brain computer interface
Hilbert spaces
Classifiers
Learning
2012
39 Citations (Scopus)
Wavelet Analysis
Hemodynamics
Frontal Lobe
Wavelet transforms
Learning algorithms
26 Citations (Scopus)

Decision support algorithm for diagnosis of ADHD using electroencephalograms

Abibullaev, B. & An, J., Aug 2012, In : Journal of Medical Systems. 36, 4, p. 2675-2688 14 p.

Research output: Contribution to journalArticle

Brain Diseases
Electroencephalography
Brain
Feature extraction
Entropy
2011
44 Citations (Scopus)

A new QRS detection method using wavelets and artificial neural networks

Abibullaev, B. & Seo, H. D., Aug 2011, In : Journal of Medical Systems. 35, 4, p. 683-691 9 p.

Research output: Contribution to journalArticle

Neural networks
Electrocardiography
Backpropagation algorithms
Feedforward neural networks
Signal analysis
16 Citations (Scopus)

Neural Network Classification of Brain Hemodynamic Responses from Four Mental Tasks

Abibullaev, B., An, J. & Moon, J. I., Oct 2011, In : International Journal of Optomechatronics. 5, 4, p. 340-359 20 p.

Research output: Contribution to journalArticle

hemodynamic responses
Hemodynamics
brain
Brain
classifiers
2010
15 Citations (Scopus)
Continuous Wavelet Transform
Spike
Wavelet transforms
Artificial Neural Network
Feature Extraction
12 Citations (Scopus)

Seizure detection in temporal lobe epileptic EEGs using the best basis wavelet functions

Abibullaev, B., Kim, M. S. & Seo, H. D., Aug 2010, In : Journal of Medical Systems. 34, 4, p. 755-765 11 p.

Research output: Contribution to journalArticle

Temporal Lobe
Electroencephalography
Epilepsy
Seizures
Wavelet decomposition
2008
6 Citations (Scopus)

Analysis of brain function and classification of sleep stage EEG using daubechies wavelet

Kim, M. S., Cho, Y. C., Berdakh, A. & Seo, H. D., 2008, In : Sensors and Materials. 20, 1, p. 1-14 14 p.

Research output: Contribution to journalArticle

sleep
electroencephalography
Electroencephalography
brain
Brain