BMI Lab develops artificial intelligence, machine-learning, and signal-processing methods for neural data. Its research addresses generalizable neural decoding, brain–machine systems, and data-driven approaches to epilepsy and neurorehabilitation.
Machine-learning approaches for robust cross-subject decoding and reduced calibration requirements.
02Deep learning, model selection, interpretability, and statistical learning for neural data.
03P300, visual evoked potentials, and brain-driven spelling and gaming interfaces.
04EEG analysis for epilepsy, source imaging, and attention-related research.
05Connecting EEG and EMG with exoskeletons, robots, and prosthetic devices.
06Measuring and classifying hemodynamic responses during cognitive and motor tasks.
07Collaborative work on neural decoding during natural infant behavior and social interaction.
Selected work on machine learning for neural data, brain–machine systems, and clinically relevant AI.
This project examines deep learning methods for detecting and localizing high-frequency oscillations in intracranial EEG, characterizing seizure dynamics, and studying seizure-onset-zone localization.
Supported by Nazarbayev University Faculty Development Competitive Research Grant · Grant No. 040225FD4727
Deep learning models that generalize across users with minimal or no individual calibration.
Motor imagery, P300, and visual-evoked-potential paradigms for brain–computer interface research.
Research on BCI-driven robotics and assistive systems for human–machine interaction.
Open tools and reproducible prototypes developed alongside the lab’s research programme.
A presurgical evaluation workbench for exploring synthetic intracranial EEG, comparing model rankings, and inspecting the signal evidence behind a finding.
A Python brain–computer interface speller project for research and teaching in neural-signal-driven communication.
Research code exploring generative approaches for augmenting EEG data in high-frequency-oscillation and seizure-onset research.
Selected videos documenting brain–computer interface experiments and research activities at BMI Lab.