To join this course you have to be enrolled as a student at TU Berlin. The IBS courses are offered alongside the course offerings of the Machine Learning Department of Faculty IV - Institute of Software Engineering and Theoretical Computer Science, TU Berlin.


The lecture series contains of 9 lectures plus two seminars at the end in which students give short presentations. We will cover fundamentals of various biosignals, timeseries pre-processing, decomposition methods, feature extraction and typical challenges in multivariate / multimodal biosignal analysis. The course is based on common methods and challenges that the Intelligent Biomedical Sensing Lab is working on towards wearable neurotechnology and brain-body imaging.

Learning Outcomes:

Knowledge and skills: Students will understand the basic concepts of common biosignals from the domains of electrophysiology (EEG, EOG, EMG, ECG, EDA), diffuse optics (fNIRS, DOT, PPG), and a few selected others. They will be familiar with filtering techniques for timeseries signal (pre-)processing, such as frequency filters, bode diagrams, fourier analysis (nyquist theorem) and wavelet analysis. They will know different frameworks for decomposition of multivariate timeseries signals, both supervised and unsupervised linear models. Basics of sensor and feature fusion and feature extraction of timeseries signals and typical challenges (artefacts, data synchronization, explainability) will be understood.


This lecture series will take place Tuesday 10-12:00 in-person in room MAR 4.033 starting October 2024.

How to register:

Please register for the course in ISIS or via email.
ISIS Link to the course ML for BSA
The course belongs to the module Machine Learning 1-X / 2-X
MOSES link to the module Machine Learning 1-X / 2-X
Check out the course offerings of the Machine Learning Department for an up to date list.


A B.Sc. degree in computer science, biomedical or electrical engineering or a comparable field is recommended. Prior attendence of Machine Learning 1 or Cognitive Algorithms and basic knowledge of linear algebra is expected.


The module grade is calculated based on
1. A short scientific presentation (20%)
2. An online test (80%)