Analyzing Neural Time Series Data

Date: 11 - 15 July 2022            
Early bird fee: €495 or €413 (deadline 1 April 2022)
Regular fee: €550
Application deadline: 1 June 2022

Download detailed programme here

Course Description

If you're interested in this course you might also want to participate in Linear Algebra for Neuroscientists.

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.

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. The course is mathematically rigorous but is approachable to researchers with no formal mathematics background. If you want to analyze 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 day will be a mix of lectures and hands-on analysis. In the analysis sessions you will have the opportunity to implement in Matlab the concepts discussed in lecture. Analysis sessions are done individually and 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.

Course Leader

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

Learning Outcomes

After this course you will be 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.

Level of participant

  • PhD
  • Post-doc
  • Professional

This course is designed for

This course is designed for 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. Masters students are welcomed if they have had some experience with neuroscience data analysis. The course focuses heavily on analog electrophysiology signals (LFP/EEG/MEG).

Admission Requirements

Previous experience (beginner to moderate level) with Matlab programming is required. A strong background in mathematics is not required. The most important requirement is a positive and optimistic attitude! The application letter should include your motivation (including a statement "By the end of this course, I want to be able to..."), and your previous experience with neuroscience data and programming languages (Matlab/python/other).

Admission Documents

  • Motivation Letter
  • CV


11 - 15 July 2022

Application Deadline

1 June 2022

Course Fee

€ 550,-

Early Bird Discounts

  • 10% early bird discount for all applicants
  • 25% discount early bird discount for:
    • students and PhD candidates from partner universities and Radboud University
    • alumni of Radboud Summer School and Radboud University

Mode of study

This course will be offered on campus.

ECTS credits


Participant Testimonials

“Very organised! The employees were very nice and helpful. The combination between learning and having fun was very balanced.”

"I recommend Mike X Cohen’s courses as he was an excellent teacher."
Katrina, 2019