We develop advanced machine learning algorithms to analyze and interpret neural signals such as EEG, EMG, fNIRS, and intracranial EEG (iEEG). Our primary objective is to gain deeper insights into brain activity and translate these findings into practical, real-world applications. This knowledge supports the development of new non-invasive and minimally invasive methods for recording and decoding neural information, with a particular focus on improving brain–computer interface (BCI) technologies.
Our work is inherently multidisciplinary, integrating robotics, computer science, biomedical engineering, and neuroscience. By combining methodologies across these domains, we aim to advance technologies that enable seamless interaction between humans and machines, and support clinical decision-making.
One of our active research directions focuses on improving surgical planning for patients with drug-resistant epilepsy. We develop deep learning models that detect and localize high-frequency oscillations (HFOs) in intracranial EEG — advanced biomarkers linked to epileptogenic tissue. Today, clinicians must manually review days of recordings, which is time-consuming and subjective.
Our AI-driven approach:
The long-term goal is to improve post-surgical seizure-free outcomes, reduce clinician workload, and expand access to expertise in regions lacking specialized neurophysiologists. This direction aligns with clinical translation, medical AI, and health-tech commercialization.
(This project is supported by Nazarbayev University under the Faculty Development Competitive Research Grant, Grant No. 040225FD4727.)
Our research contributes to:
By pushing the boundaries of neural signal interpretation, we aim to improve quality of life, expand human capability, and accelerate the future of neurotechnology-enabled systems.
A Motor Imagery Brain-Computer Interface to control a computer cursor through brain signals derived from imagining motor movements.
https://www.youtube.com/watch?v=4eJlkvwNT2U
A Motor Imagery Brain-Computer Interface to control a computer a robotic manipulator
https://youtu.be/MeZ9Orcptkc?si=T2RSGH2xTrRDWZY2
As neurotechnology continues to evolve, our lab remains committed to translating fundamental research into tangible solutions that address real-world challenges. We believe that the next frontier in human–machine interaction lies at the intersection of artificial intelligence, neuroscience, and clinical medicine.
We actively seek collaborations with clinicians, industry partners, and fellow researchers who share our vision of making neurotechnology more accessible, accurate, and impactful. Whether through advancing epilepsy care, developing next-generation BCIs, or creating innovative neurorehabilitation tools, our mission is to bridge the gap between discovery and deployment.
If you are interested in joining our research efforts, collaborating on projects, or learning more about our work, please feel free to reach out. Together, we can shape the future of neurotechnology and its role in improving human health and capability.