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Common Spatial Patterns (CSP)

A vectorized, quick and simple implementation of the CSP algorithm.

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Updated 19 Jul 2019

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The function 'csp' performs a bearable implementation of the Common Spatial Patterns (CSP) algorithm, which consists of a binary data-driven supervised data projection of a signal by maximizing the variance of the positive class while minimizing the variance of the negative one.

Input parameters:
- X1 and X2: Signals for the positive and negative class, respectively, whose dimensions must be [classes x samples].

Output parameters:
- W: Filter matrix (mixing matrix), whose columns are spatial filters.
- lambda: Eigenvalues of each filter.
- A: Demixing matrix.

Once the W is trained, the projection of new data X must be computed as:
X_csp = W'*X;

An example of use is included in the 'csp_example.m' file.

Cite As

Víctor Martínez-Cagigal (2019). Common Spatial Patterns (CSP) (https://www.mathworks.com/matlabcentral/fileexchange/72204-common-spatial-patterns-csp), MATLAB Central File Exchange. Retrieved .

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MATLAB Release Compatibility
Created with R2018a
Compatible with any release
Platform Compatibility
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