Practical course neural signals
|Language of instruction||German|
|Position within curricula||See TUMonline|
- 17.10.2016 14:00-15:00 2001, Bibliothek
- 19.10.2016 14:15-17:15 2001, Bibliothek
- 26.10.2016 14:15-17:15 2001, Bibliothek
- 02.11.2016 14:15-17:15 2001, Bibliothek
- 14.12.2016 14:15-17:15 2001, Bibliothek
- 21.12.2016 14:15-17:15 2001, Bibliothek
- 11.01.2017 14:15-17:15 2001, Bibliothek, https://portal.mytum.de/campus/roomfinder/roomfinder_viewmap?mapid=66&roomid=N0113@0101
- 18.01.2017 14:15-17:15 2001, Bibliothek, https://portal.mytum.de/campus/roomfinder/roomfinder_viewmap?mapid=66&roomid=N0113@0101
- 25.01.2017 14:15-17:15 2001, Bibliothek, https://portal.mytum.de/campus/roomfinder/roomfinder_viewmap?mapid=66&roomid=N0113@0101
- 01.02.2017 14:15-17:15 2001, Bibliothek, https://portal.mytum.de/campus/roomfinder/roomfinder_viewmap?mapid=66&roomid=N0113@0101
- 08.02.2017 14:15-17:15 2001, Bibliothek
"After the practical course students are able to: - understand theoretical foundations from neuronal responses to muscular signals , their measurement, analysis (also mulitchannel data analysis) and statistical evaluation - to set up experiments to acquire event-related potentials (e.g. auditory evoked brainstem potentials, visual ocular responses, EEG signals) - application of state-of-the-art signal processing methods, robust information extraction and evaluation of data quality of bioelectrical signals with Matlab - Conceptual understanding and Matlab implementation and evaluation of overcomplete blind source separation and classification algorithms for bioelectrical signals - statistical analysis of noisy neuronal measurement data "
"The practical module teaches fundamental methods of neuroengineering across 5 topical elements, each supervised by a different member of the CoC NeuroEngineering. In personal preparation with written material students prepare for each practical course where they will receive a hands-on practical tutorial. 1) Hemmert: Hands-on practical tutorial for understanding and measuring, statistical analysis and interpretation brainstem auditory evoked potentials (BAEPs). During the practical course, the students will record acoustically evoked BAEPs on each other. They will analyze the acquired data with Matlab (artifact rejection, filtering) and apply statistical methods to determine hearing thresholds from these noisy signals. Final presentation of the data will be in a written report, which will be graded. 2) Seeber: Construct a brain-computer interface and use neuronal measurements to control an acoustic beamformer: hear out what you are looking at! Students will measure the visual ocular response and use it control an acoustic beamformer in real time. 3) Cheng: Introduction to EEG-based multi-channel Brain-Comuter Interfaces.The written report will be graded. 4) Conradt: Attach the recording setup to a real-world device (such as small mobile robot) and develop real-time brain control of the simple mobile system; identifying appropriate levels of signal complexity, analysis of neuronal-activity patterns suited for control; possibly integration of (visual) feedback through head mounted displays; competition between different groups, such as (a) avoid obstacles; (b) follow a parcours, or (c) predator-prey games (Neuro-Robotic Olympics)
Basic programming skills in Matlab, Basic knowledge in signal processing and pattern recognition is recommended
Teaching and learning methods
Self study (preparation for course), supervised practical course, collection, analysis and presentation of the acquired data in a short summary by each participant after the course.
"The students demonstrate their ability to implement, measure, analyze and interpret (a) acoustically evoked neuronal potentials (b) brain-computer interface and use neuronal measurements to control an acoustic beamformer (c) multichannel EEG recordings (d) real-time control data extraction from noisy bioelectrical systems (e) overcomplete blind source separation and classification algorithms for bioelectrical signals. The grade for this laboratory will comprise of individual preparation for each experiment, the quality of the conducted experiment, data analysis and data presentation (report) or a written test, depending on the experiment. Grades from the 5 individual experiments will be averaged. "
"Wolpaw and Wolpaw: Brain Computer Interfaces, Principles and Practice Steven Luck: An Introduction to the Event-Related Potential Technique"