  The goal of this paper is to present a theoretical and practical introduction
to generalized eigendecomposition (GED), which is a robust and flexible
framework used for dimension reduction and source separation in multichannel
signal processing. In cognitive electrophysiology, GED is used to create
spatial filters that maximize a researcher-specified contrast, such as relative
spectral power or experiment condition differences. GED is fast and easy to
compute, performs well in simulated and real data, and is easily adaptable to a
variety of specific research goals. This paper introduces GED in a way that
ties together myriad individual publications and applications of GED in
electrophysiology. Practical considerations and issues that often arise in
applications are discussed.
