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Neural Signal Analysis and Modeling


learning results of the module

ID study objective
L1 The ability to describe and model biological and technical systems mathematically
L2 Methods of signal acquisition and processing in the fields of psychophysiology, clinical neurophysiology and audiology.
L3 Understanding of how technical components and systems operate at the bio-technical interface and in human-machine interfaces. Knowledge of neural and cognitive-affective parameters in brain-computer interfaces, neuroprosthetics and neurostimulation
L6 Fundamental principles and mechanisms of machine learning and neuro-inspired artificial intelligence (e.g. neural networks and common machine learning methods) and their applicability to practical problems. Understanding of the similarities and differences between mathematical neuron models, modern AI techniques and machine learning
L8 Central and autonomous correlates of cognitive-affective processing, with a particular focus on neuroergonomics
L9 The ability to independently source and evaluate information. Knowledge of specific sources of information in medical research.
L10 The application of the methods learnt in technical and scientific practice
L12 The ability to work in a team, key social and intercultural skills

Module name (EN):
Name of module in study programme. It should be precise and clear.
Neural Signal Analysis and Modeling
Degree programme:
Study Programme with validity of corresponding study regulations containing this module.
Neural Engineering, Master, regulation 01.10.2025
Module code: NE3201.SAM
SAP-Submodule-No.:
The exam administration creates a SAP-Submodule-No for every exam type in every module. The SAP-Submodule-No is equal for the same module in different study programs.
P213-0144, P213-0192
Hours per semester week / Teaching method:
The count of hours per week is a combination of lecture (V for German Vorlesung), exercise (U for Übung), practice (P) oder project (PA). For example a course of the form 2V+2U has 2 hours of lecture and 2 hours of exercise per week.
3V+2P (5 hours per week)
ECTS credits:
European Credit Transfer System. Points for successful completion of a course. Each ECTS point represents a workload of 30 hours.
6
Semester: 2
Mandatory course: yes
Language of instruction:
English
Assessment:
Written exam (50%), project presentation (50%)

[updated 18.06.2026]
Applicability / Curricular relevance:
All study programs (with year of the version of study regulations) containing the course.

NE2202.SAM (P213-0143, P213-0144, P213-0192) Neural Engineering, Master, regulation 01.04.2020 , semester 2, mandatory course
NE3201.SAM (P213-0144, P213-0192) Neural Engineering, Master, regulation 01.10.2025 , semester 2, mandatory course
Workload:
Workload of student for successfully completing the course. Each ECTS credit represents 30 working hours. These are the combined effort of face-to-face time, post-processing the subject of the lecture, exercises and preparation for the exam.

The total workload is distributed on the semester (01.04.-30.09. during the summer term, 01.10.-31.03. during the winter term).
75 class hours (= 56.25 clock hours) over a 15-week period.
The total student study time is 180 hours (equivalent to 6 ECTS credits).
There are therefore 123.75 hours available for class preparation and follow-up work and exam preparation.
Recommended prerequisites (modules):
None.
Recommended as prerequisite for:
Module coordinator:
Prof. Dr. Dr. Daniel Strauß
Lecturer: Prof. Dr. Dr. Daniel Strauß

[updated 29.11.2024]
Learning outcomes:
Learning Objectives: To develop skills for (part I) an advanced analysis of various types of neural signals and (part II) for modeling neural signals across spatiotemporal scales.
Topics in part I include stationary and non-stationary processing methods, source separation methods, and a mutual information analysis in multichannel recordings. Topics in part II include a review of the finite element method and neurophysical modeling. This course covers the theory but also includes hands-on exercises in both, the lab and project work. It provides students with the ability to extract information in a variety of noisy neural recordings. Moreover, the students gain a deep insight in the origin of neural signals using computational modeling. They are able to apply their computational modeling skills to new types of problems in neural engineering by means of biological and neurophysical reasoning.
Participation in actual research studies and/or lab projects to complement course topics is required. That way, students receive soft skill training related to safe patient handling.

[updated 18.07.2019]
Module content:
Part I (Signal Analysis)
 
1.  Taxonomy of Neural Signals
1.1. Review: Spontaneous vs. Evoked Activity
1.2. Spikes, Waveforms, Oscillations, Spatiotemporal Patterns
1.3. Event-Related Transient Potentials  
1.4. Evoked vs. Phase-Rest Models  
1.5. Steady-State & Stimulus Following Responses
1.6. Induced & Spontaneous Oscillatory Neural Activity
1.7. Conventional Power Band Analysis  
  
2.  Spike Processing
2.1. Spike Train Statistics
2.2. Spike Sorting Techniques
2.3. Entropy Methods
 
3.  Event-Related Neural Responses
3.1. Conventional Averaging Techniques
3.2. Single-Trial Processing & ERP Images
3.3. Manifold-Valued, Circular Data from ERPs
3.4. Amplitude & Phase Denoising of ERP Images
3.5. Inter-Trial Coherence Measures
3.6. Event-Related Synchronization/Desynchronization
3.7. Advanced ERP Pattern Recognition Schemes
3.8. Optic Flow Methods for Spatiotemporal Patterns
 
4.  Techniques for Multimodal & Multichannel Recordings
4.2. Blind Source Separation Techniques
4.3. Artifact Removal in Multichannel Recordings
4.4. Multichannel Coherence Estimation Techniques
4.5. Instantaneous Phase Dynamics
4.6. Granger Causality and Mutual Information Methods
4.7. Synchronizing Multimodal Measurements
 
5.  Applications
5.1. Invasive and Non-Invasive Brain-Computer-Interfaces
5.2. Decoding Attentional Effort Using EEG
 
Part II (Signal Modeling)
 
1.  Review of Computational Methods
2.1. Finite Element Method
2.2. The Scientific Computing Package COMSOL
2.3. Source Localization Techniques
 
2.  Neurophysical Modeling
2.1. Quasi-static approximations of Maxwell equations
2.2. Neural Sources & Volume Conduction
2.3. Discrete Neuron & Spiking Models
2.4. Local Field Potential Models
2.4. Neural Mass & Neural Field Models
2.5. Noise Models
2.6. EEG & ERP Models

[updated 15.06.2026]
Teaching methods/Media:
Blackboard, digital image projector, software

[updated 12.03.2020]
Recommended or required reading:
Bruce, Eugene N.: Biomedical Signal Processing and Signal Modeling, John Wiley & Sons, 2001
Coombes, Stephen; beim Graben, Peter (Eds.): Neural Fields: Theory and Applications, Springer, 2014, ISBN 978-3642545924
Eliasmith, Chris; Anderson, Charles H.: Neural Engineering - Computation, Representation, and Dynamics in Neurobiological Systems, MIT Press, 2003, ISBN 0-262-05071-4
Evans, J.R.; Abarbanel, A.: Introduction to Quantitative EEG and Neurofeedback, Academic Press, 1999
Gerstner, Wulfram; Kistler, Werner M.; Naud, Richard; Paninski, Liam: Neuronal Dynamics: From Single Neurons To Networks And Models Of Cognition, Cambridge Unversity Press, 2014, ISBN 978-1107635197
Hyvärinen, A.; Karhunen, J.; Oja, E.: Independent Component Analysis, John Wiley & Sons, 2001
Jin, Jian Ming: The Finite Element Method in Electromagnetics, Wiley - IEEE, 2014, ISBN 978-1118571361
Koch, Christoph: Biophysics of Computation, Oxford University Press, 2004
Mallat, Stéphane G.: A Wavelet Tour of Signal Processing, Academic Press, (akt. Aulf.)
Malmivuo, Jaakko; Plonsey, Robert: Bioelectromagnetism, Oxford University Press, 1995
Nunez, Paul L; Shrinivasan, Ramesh: Electric Fields of the Brain: the neurophysics of EEG, Oxford University Press, 1991
Semmlow, John L.: Biosignal and Biomedical Image Processing, Marcel Dekker, 2004

[updated 18.07.2019]
 
[Sat Oct 31 18:07:24 CET 2026, CKEY=nemNE2202.SAM, BKEY=nem2, CID=NE3201.SAM, LANGUAGE=en, DATE=31.10.2026]