In Brain Pattern Recognition, we'll explore all the steps involved in developing a brain-computer interface (BCI) that captures and automatically interprets brain activity. We develop these steps using a continuously developed application during the semester. An example of such an application is a BCI that automatically recognizes and responds to a person's level of mental workload from EEG data, e.g. by adaptation of the operating interface. One focus of the course is on the independent practical implementation of the theoretical concepts in the team. The objective for the participants is to understand the basic techniques of a BCI and implement them with modern tools. Course contents include brain and EEG, experiment design, signal processing, visualization, machine learning, evaluation.
Before the start of the course, a Python introduction must be completed (time-flexible) in-house and proven by processing a homework assignment.
The number of seats is limited. It is recommended to register early with Dr. med. Felix Putze (firstname.lastname@example.org).