7 August 2021
Analyzing Neural Time Series Data (August edition)
Rhythmic activity such as oscillations and synchronization are widespread in neural time series data, and are thought to have important roles in brain function, including providing temporal structure to shape information-processing, dynamically routing information processing, and synchronizing dynamics over multiple spatial and temporal scales. Detailed theories are important for understanding the role of rhythmic activity in the brain, but appropriate data analyses are absolutely essential. Unfortunately, there is often a gap between scientists' ideas about how to analyze their data, and their knowledge of the mathematical and practical steps to analyze the data in order to test those ideas.
The purpose of this course is to provide a firm grounding for understanding advanced neural time series (LFP/EEG/MEG) analyses, with a strong focus on time-frequency and synchronization analyses. It is mathematically rigorous but is approachable to researchers with no formal mathematics background. If you want to analyse your neuroscience data completely on your own, this course will certainly help get you started. It will also provide a firm basis for using analysis toolboxes such as eeglab or fieldtrip, although the course does not provide instructions for how to use these toolboxes.
Each course day will be a mix of lectures and hands-on labwork. In the labwork you will have the opportunity to implement in Matlab the concepts discussed during the lecture. Labwork is done individually or in small groups of 2-3 students. There will be homework assignments to help you consolidate and develop your newly learned skills (homework is not graded, and solutions will be provided the following day).
This will be an intensive course designed for learning, but there will be plenty of coffee and chocolates to keep you motivated.
This material has been taught by Dr. Cohen for nearly a decade in several different countries, and is the basis of the book Analyzing Neural Time Series Data (MIT Press, 2014).
You must bring a laptop with Matlab or Octave (a free Matlab-like software) installed. Desktop computers will not be available.
Dr. Michael X Cohen
Donders Institute for Brain, Cognition and Behaviour
Radboud University Medical Center
After this course you are able to :
1. Understand the mechanics of the Fourier transform and how to implement it in Matlab.
2. Use complex wavelet convolution to extract time-frequency information from time series data.
3. Simulate data to test the accuracy of data analysis methods and effects of parameters.
4. Implement non-parametric statistics to evaluate statistical significance while correcting for multiple comparisons.
EUR 0: Fee and date will be announced in the fall. Registration opens December 1st 2020. The fee includes the registration fees, course materials, access to library and IT facilities, coffee/tea, lunch, and a number of social activities.