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| ||<(|2> '''Lecturers:''' || Part 1: [[http://itb.biologie.hu-berlin.de/~kempter/|Richard Kempter]] || || Part 2: [[http://user.cs.tu-berlin.de/~blanker/|Benjamin Blankertz]], Carmen Vidaurre || |
||<(|2> '''Lecturers:''' || ''Part 1:'' [[http://itb.biologie.hu-berlin.de/~kempter/|Richard Kempter]] || || ''Part 2:'' [[http://user.cs.tu-berlin.de/~blanker/|Benjamin Blankertz]], Carmen Vidaurre || |
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| * (Part 1) Analysis of Spike Trains (Introduction to Linear Systems Theory, Introduction to Point Process Theory, Autoregressive Models for Neural Spike Trains, Correlation Analysis of Neural Spike Trains) | * ''Part 1:'' Analysis of Spike Trains (Introduction to Linear Systems Theory, Introduction to Point Process Theory, Autoregressive Models for Neural Spike Trains, Correlation Analysis of Neural Spike Trains) |
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| * (Part 2) Statistical analysis of electroencephalogram (EEG) data, e.g., investigation of event-related potentials (ERPs) and event-related desynchronization (ERD); spatial filters; classification, adaptive classifiers. | * ''Part 2:'' Statistical analysis of electroencephalogram (EEG) data, e.g., investigation of event-related potentials (ERPs) and event-related desynchronization (ERD); spatial filters; classification, adaptive classifiers. |
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| * P. Dayan and L.F. Abbott (2001) [[http://neurotheory.columbia.edu/~larry/book/|Theoretical Neuroscience]]. MIT Press, Cambridge, Massachusetts. | * P. Dayan and L.F. Abbott (2001) Theoretical Neuroscience. MIT Press, Cambridge, Massachusetts. [[http://neurotheory.columbia.edu/~larry/book/|Online]] |
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| * Gert Pfurtscheller and F. H. Lopes da Silva. Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin Neurophysiol, 110(11):1842-1857, Nov 1999. | * Pfurtscheller G, Lopes da Silva FH. Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin Neurophysiol, 110(11):1842-1857, Nov 1999. [[http://ml.cs.tu-berlin.de/bbci/papers/PfuSil99.pdf|pdf]] |
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| * Jonathan R. Wolpaw, Niels Birbaumer, Dennis J. McFarland, Gert Pfurtscheller, and Theresa M. Vaughan. Brain-computer interfaces for communication and control. Clin. Neurophysiol., 113:767-791, 2002. * Lucas C. Parra, Clay D. Spence, Adam D. Gerson, and Paul Sajda. Recipes for the linear analysis of EEG. NeuroImage, 28(2):326-341, 2005. * Benjamin Blankertz, Ryota Tomioka, Steven Lemm, Motoaki Kawanabe, and Klaus-Robert Müller. Optimizing spatial filters for robust EEG single-trial analysis. IEEE Signal Proc Magazine, 25(1):41-56, 2008. |
* Jonathan R. Wolpaw, Niels Birbaumer, Dennis J. McFarland, Gert Pfurtscheller, and Theresa M. Vaughan. Brain-computer interfaces for communication and control. Clin. Neurophysiol., 113:767-791, 2002. [[http://ml.cs.tu-berlin.de/bbci/papers/WolBirFarPfuVau02.pdf|pdf]] * Parra LC, Spence CD, Gerson AD, Sajda P. Recipes for the linear analysis of EEG. NeuroImage, 28(2):326-341, 2005. [[http://ml.cs.tu-berlin.de/bbci/papers/ParSpeGerSaj05.pdf|pdf]] * Blankertz B, Tomioka R, Lemm S, Kawanabe M, Müller KR. Optimizing spatial filters for robust EEG single-trial analysis. IEEE Signal Proc Magazine, 25(1):41-56, 2008. [[http://ida.first.fhg.de/publications/BlaTomLemKawMue08.pdf|pdf]] |
Acquisition and Analysis of Neuronal Data
Integrated lecture and tutorials
Dates, Lecturers, and Location
Dates and Rooms: |
Lecture: Fridays from 10:00 to 11:30 am in the lecture hall 102 |
Tutorials: Fridays from 12:30 to 14:00 pm in rooms 115 and/or 215 |
|
Lecturers: |
Part 1: Richard Kempter |
Part 2: Benjamin Blankertz, Carmen Vidaurre |
|
Location: |
Bernstein Center for Computational Neurosciences Berlin, Haus 6, Philippstr. 13 |
Further Information on the web page of the BCCN-B.
Topics
This part of the module "Acquisition and Analysis of Neural Data" of the Master Program in Computational Neuroscience provides knowledge on statistical analyses of neural data:
Part 1: Analysis of Spike Trains (Introduction to Linear Systems Theory, Introduction to Point Process Theory, Autoregressive Models for Neural Spike Trains, Correlation Analysis of Neural Spike Trains)
Part 2: Statistical analysis of electroencephalogram (EEG) data, e.g., investigation of event-related potentials (ERPs) and event-related desynchronization (ERD); spatial filters; classification, adaptive classifiers.
Requirements
Requirements: Basic knowledge in Neurobiology and Mathematics at the level of the first year of the Masters Program in Computational Neuroscience.
To obtain course certificates, at least 75% of the lectures (2 ECTS) must be attended, and at least 75% of the points in the exercises (5 ECTS) must be obtained.
Background material
Part 1 (spike trains)
P. Dayan and L.F. Abbott (2001) Theoretical Neuroscience. MIT Press, Cambridge, Massachusetts. Online
Part 2 (EEG)
Guido Dornhege, José del R. Millán, Thilo Hinterberger, Dennis McFarland, and Klaus-Robert Müller, editors. Toward Brain-Computer Interfacing. MIT Press, Cambridge, MA, 2007.
Pfurtscheller G, Lopes da Silva FH. Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin Neurophysiol, 110(11):1842-1857, Nov 1999. pdf
- Key AP, Dove GO, Maguire MJ. Linking brainwaves to the brain: an ERP primer. Dev Neuropsychol. 2005;27(2):183-215.
Jonathan R. Wolpaw, Niels Birbaumer, Dennis J. McFarland, Gert Pfurtscheller, and Theresa M. Vaughan. Brain-computer interfaces for communication and control. Clin. Neurophysiol., 113:767-791, 2002. pdf
Parra LC, Spence CD, Gerson AD, Sajda P. Recipes for the linear analysis of EEG. NeuroImage, 28(2):326-341, 2005. pdf
Blankertz B, Tomioka R, Lemm S, Kawanabe M, Müller KR. Optimizing spatial filters for robust EEG single-trial analysis. IEEE Signal Proc Magazine, 25(1):41-56, 2008. pdf