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13M051NI - Neural Engineering

Course specification
Course title Neural Engineering
Acronym 13M051NI
Study programme Electrical Engineering and Computing
Module Applied Mathematics, Audio and Video Communications, Audio and Video Technologies, Biomedical and Environmental Engineering, Biomedical and Nuclear Engineering, Computer Engineering and Informatics, Electronics, Electronics and Digital Systems, Energy Efficiency, Information and Communication Technologies, Microwave Engineering, Nanoelectronics and Photonics, Power Systems - Networks and Systems, Power Systems - Renewable Energy Sources, Power Systems - Substations and Power Equipment, Signals and Systems, Software Engineering, System Engineering and Radio Communications
Type of study master academic studies
Lecturer (for classes)
Lecturer/Associate (for practice)
Lecturer/Associate (for OTC)
    ESPB 6.0 Status elective
    Condition none
    The goal Learn about neurophysiological acquisition and signal processing procedures, introduce neuroimaging technologies and learn about principles of assisted technologies and rehabilitation engineering.
    The outcome Ability for research and engineering work in the field of neural engineering.
    Contents
    Contents of lectures Neural interfaces, acquisition/processing of neural signal. Brain Computer Interface. Electrical and magnetic stimulation for restoration/augmenatation of sensory-motor functions. Neural prothesis for: hearing, vision, breathing, control of movement, urinary functions, cognitive feedback. Neuroimaging technologies. Preclinical studies.
    Contents of exercises Practical work in laboratory and clinics.
    Literature
    1. Popović, DB, Sinkjær T. Control of Movement for the Physicaly Disabled, Springer, 2000, London, U,.K
    2. Dhilon G, Horch K. (Eds.) Neuroprosthetics: Theory and Practice, 2004, World Sci Publ, New York
    3. Ombao H, Lindquist M, Thompson W, Aston J, Handbook of Neuroimaging Data Analysis. CRC Press; 2016.
    4. Akay, Metin, ed. Handbook of neural engineering. Vol. 21. John Wiley & Sons, 2007.
    5. Bin He, Neural Engineering, Springer Cham, 2020.
    Number of hours per week during the semester/trimester/year
    Lectures Exercises OTC Study and Research Other classes
    3 1
    Methods of teaching Lectures, exercises with consulting, independent work on the project.
    Knowledge score (maximum points 100)
    Pre obligations Points Final exam Points
    Activites during lectures Test paper
    Practical lessons 20 Oral examination 30
    Projects
    Colloquia 20
    Seminars 30