2010 – Ph.D. Yeungnam University, South Korea
2006 – M.Sc. Yeungnam University, South Korea
2004 – B.Sc. Tashkent University of Information Technologies, Uzbekistan
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 Samsung Medical Center and was appointed as a research professor at Sungkyunkwan University in Seoul. He was also 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 on developing novel neural interfaces for neurorehabilitation in poststroke patients.
Dr. Abibullaev has considerable research and clinical experience working with patient populations and physicians in applying scientific and technical skills to advance the development of treatments for neurological disorders. His research focuses on developing new non-invasive Brain-Machine Interfaces (BMI) to (1) understand human brain function and cognitive states in health and disease, (2) enable bi-directional communication between brains and machines (3) develop neuroprosthetics to restore locomotion and motor function in patients with subcortical stroke.
Brain-Computer/Machine Interfaces, Kernel based Machine Learning, Neural Engineering, Electromagnetic Source Imaging.
1. J.G. Cruz-Garza, Z.R. Hernandez, T. Tse, E. Caducoy, B. Abibullaev, J.L. Contreras-Vidal. A novel experimental and analytical approach to the multimodal neural decoding of intent during social interaction in freely-behaving human infants. Journal of Visualized Experiments, doi: 10.3791/53406, October, 2015.
2. C.H. Park, J.H Seo, D. Kim, B. Abibullaev, H. Kwon, Y.H. Lee, M.Y. Kim, K. Kim, J.S. Kim, E.Y. Joo, S.B. Hong, (2015, Feb). EEG Source Imaging in Partial Epilepsy in Comparison with Presurgical Evaluation and Magnetoencephalography. Journal of Clinical Neurology, 2015 Feb 17, 11:e12.
3. B. Abibullaev, J An, S.H. Lee, S.H. Jin, and J.I. Moon. Minimizing inter-subject variability in fnirs based brain computer interfaces via multiple-kernel support vector learning. DOI: 10.1016/j.medengphy.2013.08.009, Medical Engineering Physics, 2013. Elsevier.
4. B. Abibullaev and J. An. Classification of frontal cortex hemodynamic response during cognitive tasks using wavelet transforms and machine learning algorithms. Medical Engineering Physics, 34(10):1394–410, 2012. Elsevier.
5. B. Abibullaev and J. An. Decision support algorithm for diagnosis of ADHD disorder using electroencephalograms. Journal of Medical Systems, 36(4):2675–2688, 2011. Springer.
6. B. Abibullaev, J. An, and J.I. Moon. Neural network classification of brain hemodynamic responses from four mental tasks. Int J Optomechatronics, 5(4):340–359, 2011. Taylor & Francis.