Research
Budapest University of Technology and Economics
Faculty of Electrical Engineering and Informatics
Cognitive computing
biofeedback • cognitive • ecg • eeg • Item response theory • learning • mental workload
Activity of the research group:
The research group aims to develop an integrated, psychometric and technology-based research framework that serves the understanding, diagnosis, and development of learning processes.
Our main research directions include the design of psychometric models for both diagnostic and developmental purposes, with particular attention to modeling permanent and transient learning disorders and disabilities. In the field of personalized cognitive training, we develop parameter-level, CHC-compatible adaptive mechanisms that integrate measurement and development functions — enabling the system to simultaneously perform diagnostic profiling and targeted cognitive enhancement.
A key outcome of our work is the synthetic data generator, which produces detailed, high-resolution datasets suitable for training neural networks, allowing the joint validation of psychometric and machine learning models.
Our research also includes the development of personalized learning environments based on wearable physiological sensors (ECG, EEG, pupillometry) and augmented (AR) and virtual reality (VR) technologies.
Recent results:
- Development of psychometric and cognitive models: modeling learning disorders, ability profiles, and developmental pathways
- Framework design: process identification, definition of variables, and the relationships between them
- Preprocessing algorithm for the unified classification of fMRI images
- Definition of mental states and the design of profile curves and classifiers based on ECG, EEG, and pupillometric data
- Design and implementation of a software package for the validation of research results
- Creation of experimental, game-based software suites for educational, diagnostic, and cognitive skill development purposes