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The goal of this paper is to present a theoretical and practical introductionto generalized eigendecomposition (GED), which is a robust and flexibleframework used for dimension reduction and source separation in multichannelsignal processing. In cognitive electrophysiology, GED is used to createspatial filters that maximize a researcher-specified contrast. For example, onemay wish to exploit an assumption that different sources have differentfrequency content, or that sources vary in magnitude across experimentalconditions. GED is fast and easy to compute, performs well in simulated andreal data, and is easily adaptable to a variety of specific research goals.This paper introduces GED in a way that ties together myriad individualpublications and applications of GED in electrophysiology, and provides sampleMATLAB and Python code that can be tested and adapted. Practical considerationsand issues that often arise in applications are discussed.

A tutorial on generalized eigendecomposition for denoising, contrast enhancement, and dimension reduction in multichannel electrophysiology