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

TL;DR

This paper provides a comprehensive introduction to generalized eigendecomposition (GED) for multichannel electrophysiology, highlighting its theoretical basis, practical applications, and providing code examples for researchers.

Contribution

It offers a unified overview of GED's use in electrophysiology, combining theory, practical guidance, and code resources for diverse research applications.

Findings

GED is fast and easy to compute.

GED performs well on simulated and real data.

GED effectively creates spatial filters for source separation.

Abstract

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. For example, one may wish to exploit an assumption that different sources have different frequency content, or that sources vary in magnitude across experimental conditions. 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, and provides sample MATLAB and Python code that can be tested and adapted. Practical considerations and issues that often arise in applications are discussed.