Integrated lecture and tutorials ''Acquisition and Analysis of Neuronal Data''
Dates, Lecturers, and Location
Module |
Part of the Master Program Computation Neuroscience; BCCN Modulkatalog |
Dates and Rooms: |
Lecture: Fridays from 09:15 to 10:45 in the lecture hall 102 (Haus 6) |
Tutorials: Fridays from 11:00 (st!) to 12:30 in the computer pool of Haus 2 |
|
Lecturers: |
Part 1: Richard Kempter |
Part 2: Benjamin Blankertz, Matthias Schultze-Kraft |
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Location: |
Bernstein Center for Computational Neurosciences Berlin, Haus 6 (lecture) and Haus 2 (tutorials), Philippstr. 13 |
TU University calendar: |
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 (spike statistics, neural coding, theory of point processes, linear systems theory, correlation analysis, spike-triggered average, reverse correlation, STRF, neural decoding, signal detection theory, infomation theory, signal-to-noise ratio analysis) - see here for details on the first part.
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.
Required background knowledge: Basic knowledge in Neurobiology and Mathematics at the level of the first year of the Master Program in Computational Neuroscience.
Course Certificates
To obtain course certificates, at least 75% of the points in the exercises (5 ECTS) must be attained.
To obtain the full 5 ECTS for the tutorial, every student has to complete an additional small project (2 ECTS). The tasks of the projects will be distributed at the end of the course. Solutions are to be submitted until two weeks before the oral exam.
The final oral exam of the module "Acquisition and Analysis of Neuronal Data will take place October 2nd 2014.
Material
Removed. See at more recent courses.
Projects
To obtain the full 5 ECTS for the tutorial, every individual student has to complete an additional small programming project. Project topics will be distributed at the end of the lecture series, and every student should work on her/his topic in the lecture-free time (July/August/September 2014).
Students are required to turn in a short written report, that is, a self-contained description of results. The report should comprise a single PDF file (including an Introduction, a short description of the experiment, a Results/Discussion section as well as labeled Figures with captions) and the Matlab code as an attachment. Guidelines for Writing a Scientific Report might be helpful.
An overview of the projects is available here.
Project BCI #01: Rapid Serial Visual Presentation - Tasks | Paper 1, Paper 2 | Data: Matlab, Python
Project BCI #02: Motor Imagery based BCI - Tasks | Paper 1, Paper 2 | Data: Matlab, Python
Project BCI #03: Covert Shifts of Visual Attention - Tasks | Paper 1, Paper 2 | Data: Matlab, Python
Project BCI #04: ERP Speller based on Spatial Auditory Attention - Tasks | Paper 1, Paper 2 | Data: Matlab, Python
Project BCI #05: ERP-based Speller - Tasks | Paper | Data: Matlab, Python
Project BCI #06: 2D Auditory Speller - Tasks | Paper | Data: Matlab, Python
Background material for Part 1 (BCI)
Blankertz B, Lemm S, Treder MS, Haufe S, Müller KR. Single-trial analysis and classification of ERP components - a tutorial. NeuroImage, 56:814-825, 2011. pdf url
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 url
Treder MS, Blankertz B, (C)overt attention and visual speller design in an ERP-based brain-computer interface. Behav Brain Funct, 6:28, 2010. url
Parra LC, Spence CD, Gerson AD, Sajda P. Recipes for the linear analysis of EEG. NeuroImage, 28(2):326-341, 2005. pdf
Parra LC, Christoforou C, Gerson AD, Dyrholm M, Luo A, Wagner M, Philastides M, Sajda P. Spatiotemporal Linear Decoding of Brain State. IEEE Signal Proc Magazine, 25(1): 107-115, 2008. pdf
Key AP, Dove GO, Maguire MJ. Linking brainwaves to the brain: an ERP primer. Dev Neuropsychol. 2005;27(2):183-215. pdf
Pfurtscheller G, Lopes da Silva FH. Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin Neurophysiol, 110(11):1842-1857, Nov 1999. pdf
Wolpaw JR, Birbaumer N, McFarland DJ, Pfurtscheller G, Vaughan TM. Brain-computer interfaces for communication and control. Clin. Neurophysiol., 113:767-791, 2002. pdf
Dornhege G, Millán J del R, Hinterberger T, McFarland DJ, Müller KR, editors. Toward Brain-Computer Interfacing. MIT Press, Cambridge, MA, 2007.
Wolpaw JR, Wolpaw EW, editors. Brain-Computer Interfaces: Principles and Practice. Oxford University Press, 2012. ISBN-13: 978-0195388855.