Rosipal R., Porubcova N., Cimrova B., Farkas I.

To improve upper-limb neuro-rehabilitation in chronic stroke patients we apply new methods and tools of clinical training and machine learning for the design and development of an intelligent system allowing the users to go through the process of self-controlled training of impaired motor pathways. We combine the brain-computer interface (BCI) technology with a robotic arm system into a compact system that can be used as a robot-assisted neuro-rehabilitation tool: (1) We use mirror therapy (MT) not only to improve motor functions but also to identify subject's "atoms", i.e. spectral-spatial EEG patterns associated with imagined or real-hand movements, using parallel factor analysis. (2) We designed and tested a BCI-based robotic system using motor imagery in a patient with an impaired right upper limb. The novelty of this approach lies in the control protocol which uses spatial and spectral weights of the estimated sensorimotor atoms during the MT sessions.