Integrated lecture and tutorials ''Acquisition and Analysis of Neuronal Data''

Dates, Lecturers, and Location

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: 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: 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.

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 project in AAND will be distributed in the lecture on July 6th and put on this web page. The tasks will be similar to the exercises (EEG data analysis with Matlab) and solutions have to be submitted until October 1st. The idea is to have small groups of two or three students who work together on the tasks. Each group will receive a different task.

The data for the final oral exam on the module "Acquisition and Analysis of Neuronal Data" will be announced here.

Material

will be provided here.

Background material

Part 2 (EEG)

• 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.