htw saar Piktogramm
Back to Main Page

Choose Module Version:


Neural Signal Analysis and Modeling

Module name (EN): Neural Signal Analysis and Modeling
Degree programme: Neural Engineering, Master, ASPO 01.04.2020
Module code: NE2202.SAM
Hours per semester week / Teaching method: 3V+2P (5 hours per week)
ECTS credits: 6
Semester: 2
Mandatory course: yes
Language of instruction:
Oral exam (50%), project work (50%)

[updated 12.03.2020]
Curricular relevance:
NE2202.SAM Neural Engineering, Master, ASPO 01.04.2020, semester 2, mandatory course
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):
Recommended as prerequisite for:
Module coordinator:
Prof. Dr. Dr. Daniel Strauß
Prof. Dr. Dr. Daniel Strauß

[updated 12.03.2020]
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 18.07.2019]
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]
[Sun Aug  1 14:59:10 CEST 2021, CKEY=nemNE2202.SAM, BKEY=nem, CID=NE2202.SAM, LANGUAGE=en, DATE=01.08.2021]