Consulting and research in the field of muscle-machine and brain-machine interactions.

• Objectives: After the training, participants will be able to evaluate advanced muscle-machine and brain-machine interfaces; evaluate the potential and usability of the muscle-machine and brain-machine interfaces; design computer-aided biomedical signal pre-treatment procedures; use and evaluate algorithms for source separation and classification of biomedical signals.
• Content: In the course of the training the participants will get to know the current state of knowledge in the field of advanced human-machine communication. They will get acquainted with the procedures for non-invasive capture and interpretation of commands i.e., neural codes, with the central nervous system controls the functioning of skeletal muscles. We will learn about computer procedures for pre-treatment, cleavage, and classification of noninvasively recorded electrical muscle activities. We will review possible applications of this technology in the fields of neuroscience, rehabilitation, neurology, ergonomics, sports, recreation, and consumer electronics.
•  Duration: 3 Days (24 School hours)
• Who is this training designed for: The training is intended for technicians, planners, developers and specialists in the fields of neuroscience, rehabilitation, neurology, ergonomics, sports, recreation and consumer electronics.
• Organizer: UM FERI, Institute of Computer Science, System Software Laboratory
• Lecturer: prof. dr. Aleš Holobar and associates of the System Software Laboratory

• OttoBock (Germany),
• Ossur (Iceland)

Other current projects

• MIO-A – Exact quantification of muscle control strategies and co-activation patterns in robot-assisted rehabilitation of hemiparetic patients
• Decomposition of skeletal muscle tensiomyogram with identification of contractile paramaters for sensitive monitoring of muscle adaptation
• Decomposition of Compound Muscle Action Potentials

• Urh F. and Holobar A. (2020): Automatic identification of individual motor unit firing accuracy from high-density surface electromyograms.
• Potočnik B., Divjak M., Urh F., Frančič A., Kranjec J., Šavc M., Cikajlo I., Matjačić Z., Zdravec M. and Hoblar A. (2020): Estimation of muscle co-activations in wrist rehabilitation after stroke is sensitive to motor unit distribution and action potential shapes. 
• Holobar A., Gallego J. A., Kranjec J., Rocon E., Romero J. P., Benito- Leon J., Pons J. L. and Glaser V. (2018): Motor unit-driven identification of pathological tremor in electroencephalograms.
• Šavc M., Glaser V., Kranjec J., Cikaljo I., Matjačić Z. and Holobar A. (2018): Comparison of convolutive kernel compensation and non-negative matrix factorization of surface electromyograms.

HOLOBAR, Aleš. Postopek in naprava za dekompozicijo sestavljenih mišičnih akcijskih potencialov : patentna prijava P-201900252, z dne 19. 12. 2019

HOLOBAR, Aleš, KRANJEC, Jernej. Computer apparatus and procedures for the direct evaluation of skeletal muscle excitation patterns from electromyograms / Računalniška naprava za neposredno ocenjevanje vzorcev vzbujanja skeletnih mišic in elektromiogramov : patentna prijava P-201900026, z dne 7. 2. 2019

Evaluation of electrical activities of skeletal muscles and cortex in human-machine interaction.