PCA & multivariate signal processing, applied to neural data
- 10 hours on-demand video
- 9 articles
- 8 downloadable resources
- Full lifetime access
- Access on mobile and TV
- Certificate of Completion
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- Understand advanced linear algebra methods
- Apply advanced linear algebra methods in MATLAB
- 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
- Some linear algebra background (or interest in learning!)
- Some neuroscience background (or interest in learning!)
- Some MATLAB 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. MATLAB is the primary numerical processing engine but the material is easily portable to Python or any other language.
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!
- 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
Figure out if this course is right for you, and if so, how best to learn from this course.
Learn the general goals of neuroscience research, and why neuroscience is moving towards big multivariate datasets.
Know the several interpretations of the word “dimension,” and the definition used in this course.
Learn the element-wise and matrix equations for covariances. Have some pointers for what to look for when viewing covariance matrices.
Increase spatial precision by reducing large dimensions using (partial) whitening.