Does data cleaning improve brain state classification?

ABSTRACT:

Background

Neuroscientists routinely seek to identify and remove noisy or artifactual observations from their data. They do so with the belief that removing such data improves power to detect relations between neural activity and behavior, which are often subtle and can be overwhelmed by noise. Whereas standard methods can exclude certain well-defined noise sources (e.g., 50/60 Hz electrical noise), in many situations there is not a clear difference between noise and signals so it is not obvious how to separate the two. Here we ask whether methods routinely used to "clean" human electrophysiological recordings lead to greater power to detect brain-behavior relations.

New method

This, to the authors' knowledge, is the first large-scale simultaneous evaluation of multiple comm

SUBMITTER: Meisler SL 

PROVIDER: S-EPMC11225530 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature