Cognitive Human-Machine Interaction

The exciting world of Human–Machine Interaction (HMI) involves the study of the interaction between the user and the machine. Here we see how computer science combines with behavioral sciences and results in interaction at the user interface which includes both software and hardware.  

We propose novel methods and algorithms for time-series analysis that can be successfully applied for real-time brain states classification from Electroencephalogram EEG and can be integrated in real-time EEG-enabled systems. Current approach in brain state recognition algorithms development is to propose new features (including non-linear), feature extraction algorithms and/or learn features using deep learning techniques and study different neural network systems to improve accuracy of brain states recognition. The proposed algorithms can be used for optimisation of human-machine interfaces in rehabilitation systems, entertainment, robotics and more.

Areas of Expertise


Cognitive Abilities Enhancement

The development of novel algorithms and techniques to assess cognitive abilities from EEG and tracking improvements using 3D neurofeedback games.


Human Factors Evaluation

Using EEG to quantify human emotions, mental workload, stress, and attention in real time.


Affective Computing

Experts in EEG-based emotional state recognition, Fraunhofer Singapore develop novel algorithms that accurately recognise 8 emotional states.


Facial Expression Recognition

Captured facial images can become a source for emotion recognition and serve as a complement to physiological signal-based recognition methods.


Transfer Learning in AI Systems

A transferable AI differs from traditional AI in its use of pre-trained models to "jump start" the development of AI for a new task or problem.  

Skills Evaluation to Enhance Training

We developed an innovative human competence model that could be integrated in any Virtual Reality training system.


Stress and Fatigue Monitoring in VR Environments

Leveraging on eye tracking technology, we developed an innovative real-time stress and fatigue monitoring algorithms as an additional layer of evaluation, utilizing novel machine learning algorithms in VR application.

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