Swedish

If sufficient demand

Demonstrate understanding in signal processing and applications for different types of brain computer interfaces, with focus towards EEG.

Different techniques for recording of brain activity (EEG, fMRI, invasive, non-invasive), event-related potentials (ERP), motor imagery/sensorimotor rhythms (MI/SMR), Steady-State Evoked Potentials (SSxEP), feature extraction, linear discriminant analysis (LDA), common spatial patterns (CSP), Riemannian geometry (RG), bayesian learning (BL), transfer learning (TF), BCI calibration.

Demonstrate understanding of the most commonly used BCI paradigms and how basic types of signal processing and machine learning can be applied to those paradigms, here with a focus towards EEG-based BCI systems.

Master tools from existing Python packages such as MNE python, pyriemann, sklearn, and timeflux, to process, analyze, and/or visualize EEG-data using techniques covered in the course.

Demonstrate understanding of the limitations of different types of BCI paradigms and commonly used methods for signal processing of such paradigms, and be able to organize and select material for discussion seminars.

Project

Self-study literature review

Seminars given by participants

Four seminars that span different parts of the course. Completion of a small project.

Failed, pass

Nam, C., Nijholt, A. & Lotte, F.: Brain–Computer Interfaces Handbook, Technological and Theoretical Advances.. 2018. ISBN 9781498773430.

FRT280F

2022-06-14

Professor Thomas Johansson