Analyzing Neural Time Series Data

Do you want to learn more about neural time series analysis but are missing a formal background in mathematics? Then this course is for you! You will learn the foundational concepts underlying spectral and synchronization analyses, and how to implement them in MATLAB.

Course leader

Dr. Michael X Cohen Associate professor Donders Institute for Brain, Cognition and Behaviour Radboud University Medical Center

Target group

• PhD • Post-doc • Professional PhD students, postdocs, and senior researchers who have experience with data analysis and want a deeper understanding of advanced data analysis methods. Some experience with Matlab is necessary. Master's students are welcomed if they have had some experience with neuroscience data analysis. The course focuses heavily on analog electrophysiology signals (LFP/EEG/MEG).

Course aim

After this course you are able to: • Understand the mechanics of the Fourier transform and how to implement it in Matlab. • Use complex wavelet convolution to extract time-frequency information from time series data. • Simulate data to test the accuracy of data analysis methods and effects of parameters. • Implement non-parametric statistics to evaluate statistical significance while correcting for multiple comparisons.