Interacting from the brain to EEG signals (EEG source reconstruction)

Note: all our courses are taught online this year.

Electroencephalogram (EEG) is one of the neuro-imaging techniques that is widely used in research and clinical practice to study brain activity. It allows measuring neural processes with high temporal but relatively low spatial resolution. Source localization can be used to improve spatial resolution and localize the neural activity in the brain. However, source localisation is difficult due to its so-called inverse problem (inference of the position of the current sources from electrode potentials). Beamforming is one of the various solutions to this problem that has gained attention in recent years.

Course days11-15 January 2021
Course levelMaster, PhD candidates and professionals from all disciplines 
Coordinating lecturerMarzieh Borhanazad, MSc; Prof. Andreas Daffertshofer
Other lecturersNone
Forms of tuitionStudents will be instructed through the lectures, short demos, Matlab assignments, and reading materials.
Forms of assessmentThe assessment will be based on the assignments (30%) and a final examination (70%).
Credits3 ECTS
Contact hours25 hours
Tuition feeRead all information about our tuition fees and what's included here 
How to applyFind our application form here
The target audience is all students that are interested in EEG and analysing the EEG signals. You are required to have experience with programming in Matlab including its signal processing toolbox. A basic knowledge of statistics is beneficial. If you have doubts about your eligibility for the course, please contact us: [email protected]

Electroencephalogram (EEG) is one of the neuro-imaging techniques that is widely used in research and clinical practice to study brain activity. It allows measuring neural processes with high temporal but relatively low spatial resolution. Source localization can be used to improve spatial resolution and localize the neural activity in the brain. However, source localisation is difficult due to its so-called inverse problem (inference of the position of the current sources from electrode potentials). Beamforming is one of the various solutions to this problem that has gained attention in recent years.

In this course, we will address the (mathematical) issues and solutions for localising the neural sources in the brain. The course is for students who want to get involved in analysing EEG signals and learn state-of-the-art techniques in EEG source analysis. The course will last for 4 days. Each day starts with a lecture followed by practical (using Matlab) and self-study. We will also use the open-access Fieldtrip toolbox (http://www.fieldtriptoolbox.org/) for analysing the EEG data. 

Day 1: Introduction to EEG and EEG analysis.

Day 2: Spectral analysis.

Day 3: Forward and inverse modelling.

Day 4: Beamforming analysis.

Day 5: Q&A and examination. 


  • Introduction to EEG and EEG analysis:
    The students will be presented with the introduction on fundamentals of EEG signals, neural origins of EEG, quality of signals, and get to know EEG artifacts and cleaning the EEG signals. They will be provided with references to further study and gain more detailed knowledge.
  • Spectral analysis:
    The students will learn to apply frequency analysis on the EEG signals by understanding Fourier transform and windowing.
  • Forward and Inverse Modelling:
    The students will learn the problems prevalent in source localisation in EEG namely forward and inverse problem and different solution techniques for forward modelling: Boundary Element Method (BEM), Finite Element Method (FEM) and Finite Difference Method (FDM) as well as inverse modelling to locate brain activity by means of Beamforming. 
  • Beamforming Analysis:
    The students will learn the main aspects of beamforming and different beamforming techniques mainly dynamic imaging of coherent sources (DICS). 
  • Q&A and Examination:
    In the last session, students have the opportunity to ask their questions about the entire course and later get ready for the examination.  

The course will be a combination of 15% classroom lectures and 15% practical sessions and 70% self-study.