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Research Engineers/Scientist, Research Fellows and Senior Research Fellow


Neurometrics & eTraining Platform

EEG signals, behavioural data, psychophysiological data (e.g., heart rate, GSR) and facial expression will be collected from participants while they are undertaking computerized tasks and virtual-reality games that of real-world complexity. Deep learning algorithms will be used to extract and model relevant features from the multimodal data to arrive at an objective and accurate assessment of performance (i.e., individual continuous training portfolio). The next generation training system will be a closed loop, representing learning that takes place continuously both ways between the computer system and the user. 

Enhanced Bidirectional Brain Machine Interface (BxI) – Integrating Human with Machines

The researches of this project will provide deeper insights into neural mechanisms underlying brain machine interface and algorithms used for decoding physiological signals. Moreover, an enhanced bidirectional brain machine interface system will be developed, with which users could map their mental activities and bodily movements into a humanoid robot in a more natural and realistic manner.

Research Skills

•       Neuroergonomics/ Experimental Psychology training protocols and programing (E-prime, PsychoPy)

•       Multimodal Electrophysiological Signals (EEG, EMG, HRV and GSR) Processing and Analysis

•       Mapping and Analysis of Brain Networks Connectivity

•       Machine Learning Algorithms to understand  brain electrophysiological data 

•       Computational Intelligenceand Neural Networks Algorithms in Cognitive Neuroscience and Neuroergonomics

•       Deep Learning Algorithms with applications in Human Machine Interactions

Those who are interested can send their CV with a motivation letter to Prof. Tassos Bezerianos (email address