After successfully finishing the course students can independently:|
• Explain the origin and properties of the brain and EEG signals
• Explain and apply methods for analysing and classifying ERPs in EEG signals.
• Explain different types BCI paradigms and apply them in a BCI.
• Conduct neurophysiological data analysis using tools such as EEGLab or programming languages such as Matlab.
The course gives an introduction to Brain Computer Interfacing (BCI). The course will be geared towards knowledge and give an introduction into several BCI paradigms -- such as SSVEP, P300, N400, Imaginary movement -- signal acquisition, pre-processing techniques, classification methods and user feedback. Moreover, attention will be paid to the integration of standard Human Computer Interaction methods into the BCI domain. Students will be graded on basis of their scientific portfolio which includes assignments, weekly summaries, and presentations.|
• Knowledge of real time systems, for example the course Real Time Systems (192130200)
• Basic Knowledge of (Digital) Signal Processing