Discriminative dimensionality reduction for analyzing EEG data

Eunho Noh	University of California, San Diego
Virginia de Sa	University of California, San Diego

Abstract

We propose a novel way to use discriminative analysis to project high-dimensional EEG data onto a low-dimensional discriminative space for visualization, analysis, and statistical testing. This multivariate analysis directly controls for the multiple comparison problem (MCP) by effectively reducing the number of test variables. A major advantage of this approach is that it is possible to compare the brain activity across conditions even when the trial count is low, provided that a sufficient number of trials are used to establish the initial hyperplane(s), meaning that error conditions and conditions that divide subtle behavioral differences can be readily compared. Currently these data are either ignored or lumped with other data thereby losing the ability to reveal the neural mechanisms underlying subtle behavioral differences. The proposed method provides a powerful tool to analyze conditions with relatively small numbers of trials from high-dimensional neural recordings.

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