Acquisition and Analysis of Neural Data

Students will gain knowledge about the most important methods for experimental acquisition of neural data and the respective analytical methods. Students will learn about the different fields of application, the advantages and disadvantages of the different methods and will become familiar with the respective raw data. They will be enabled to choose the most appropriate analysis method and apply them to experimental data.

For a complete module description, see the full module catalogue here.

Theoretical Lecture and Computer Tutorial

Fridays: Lecture - 09:15 - 10:45, BCCN Lecture Hall (house 6)

             Tutorial - 11:00 (sharp) - 12:30, BCCN Computer Pool (house 2)

Lecture: Richard Kempter, Benjamin Blankertz
Tutorial on EEG data (1st part): Benjamin Blankertz
Tutorial on spike trains (2nd part): N.N.

Target Group: Students of Computational Neuroscience, Medical Neuroscience, Biology, Biophysics, Physics, Mathematics, and Computer Science.

Requirements: Basic knowledge in Neurobiology and Mathematics at the level of the first year of the Masters Program in Computational Neuroscience.

Topics: This course is the second part of the module ''Acquisition and Analysis of Neural Data'' of the Master Program in Computational Neuroscience, and this second part focuses on statistical analyses of neural data:

(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.

(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).

Course Certificates: To obtain a course certificate, at least 75% of the points in the weekly exercises must be obtained.

To obtain the full 5 ECTS for the tutorial, every student has to complete an additional small project (2 ECTS).

For information and updates, see lecturers' teaching pages:

Benjamin Blankertz' page

Richard Kempter's page


Date LecturerTopic
Fri, Apr 12, 2013  Benjamin BlankertzOverview of BCI; characterization of Gaussian distribution
Fri, Apr 19, 2013  Benjamin BlankertzERP-based BCIs; spatio-temporal features; LDA
Fri, Apr 26, 2013  Benjamin BlankertzShrinkage of the empirical covariance matrix
Fri, May 3, 2013  Benjamin BlankertzLinear models of EEG; spatial patterns and spatial filters
Fri, May 10, 2013  no class 
Fri, May 17, 2013  Benjamin BlankertzModulation of brain rhythms; common spatial pattern analysis
Fri, May 24, 2013  Benjamin BlankertzAdaptive classification (supervised and unsupervised)
Fri, May 31, 2013  Richard KempterThe firing rate
Fri, Jun 7, 2013  Richard KempterSpike-train statistics: spike-triggered average and reverse correlation
Fri, Jun 14, 2013  Richard KempterCorrelation functions and the neural code
Fri, Jun 21, 2013  Richard KempterNeural encoding
Fri, Jun 28, 2013  Richard KempterNeural encoding
Fri, Jul 5, 2013  Richard KempterInformation theory
Fri, Jul 12, 2013  no class