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PCA & multivariate signal processing, applied to neural data

Learn and apply cutting-edge data analysis techniques for “big neurodata” (theory and MATLAB/Python code)

What you’ll learn

  • Understand advanced linear algebra methods
  • Includes a 3+ hour “crash course” on linear algebra
  • Apply advanced linear algebra methods in MATLAB and Python
  • Simulate multivariate data for testing analysis methods
  • Analyzing multivariate time series datasets
  • Appreciate the challenges neuroscientists are struggling with!
  • Learn about modern neuroscience data analysis

Course content

17.5 total hours


  • Some linear algebra background (3+ hour crash course is provided)
  • Some neuroscience background (or interest in learning!)
  • Some MATLAB/Python programming experience (only to complete exercises)
  • Interest in learning applied linear algebra


What is this course all about?

Neuroscience (brain science) is changing — new brain-imaging technologies are allowing increasingly huge data sets, but analyzing the resulting Big Data is one of the biggest struggles in modern neuroscience (if don’t believe me, ask a neuroscientist!).

The increases in the number of simultaneously recorded data channels allows new discoveries about spatiotemporal structure in the brain, but also presents new challenges for data analyses. Because data are stored in matrices, algorithms developed in linear algebra are extremely useful. 

The purpose of this course is to teach you some matrix-based data analysis methods in neural time series data, with a focus on multivariate dimensionality reduction and source-separation methods. This includes covariance matrices, principal components analysis (PCA), generalized eigendecomposition (even better than PCA!), and independent components analysis (ICA). The course is mathematically rigorous but is approachable to individuals with no formal mathematics background. The course comes with MATLAB and Python code (note that the videos show the MATLAB code and the Python code is a close match).

You should take this course if you are a…

  • neuroscience researcher who is looking for ways to analyze your multivariate data.

  • student who wants to be competitive for a neuroscience PhD or postdoc position.

  • non-neuroscientist who is interested in learning more about the big questions in modern brain science.

  • independent learner who wants to advance your linear algebra knowledge.

  • mathematician, engineer, or physicist who is curious about applied matrix decompositions in neuroscience.

  • person who wants to learn more about principal components analysis (PCA) and/or independent components analysis (ICA)

  • intrigued by the image that starts off the Course Preview and want to know what it means! (The answers are in this course!)

Unsure if this course is right for you?

I worked hard to make this course accessible to anyone with at least minimal linear algebra and programming background. But this course is not right for everyone. Check out the preview videos and feel free to contact me if you have any questions.

I look forward to seeing you in the course!

Who this course is for:

  • Anyone interested in next-generation neuroscience data analyses
  • Learners with interest in applied linear algebra to modern big-data challenges
  • Neuroscientists dealing with “big data”
  • Mathematicians, engineers, and physicists who are interested in learning about neuroscience data

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