Connecting Brains and Computers: Theory, Practice and Applications

Full course description

The analysis of brain activation online (i.e., during ongoing data acquisition) allows for brain-computer interfacing. A brain-computer interface (BCI) connects a brain with a computer. It can ‘translate’ brain activation as measured with (almost) any functional-neuroimaging method (e.g., electroencephalography [EEG], functional magnetic resonance imaging [fMRI] and functional near-infrared spectroscopy [fNIRS]) into digital code (i.e., computer signals). These computer signals can be interpreted as different ‘commands’ for motor-independently controlling external devices (e.g., robotic hand or spelling system) that can aid severely paralyzed patients. Moreover, it allows for providing individuals with information about their ongoing brain processes (‘neurofeedback’). This not only creates fascinating research possibilities in fundamental neuroscience but also opens up the opportunity to develop brain-based therapies for the treatment of brain disorder and dysfunction.

This elective will introduce the students to the general technical/methodological requirements, problems/challenges and application possibilities of brain-computer interfacing. Besides attending lectures, in which course participants will be provided with basic relevant knowledge by local BCI researchers, students will study seminal papers of recent BCI work. Further, students will discuss the pros and cons of different functional brain imaging methods employed for BCIs as well as ethical implications and future directions. The practical part of this Elective course will start with a demonstration of a BCI experiment. Finally, the students will record and analyze in real-time fNIRS data themselves.

At the end of this course, students will have obtained fundamental knowledge of the methodology, limitations and the application potential and implications of brain-computer interfacing. Finally, future BCI developments will be discussed.

The final assessment for this course is a numerical grade between 0,0 and 10,0.