Machine learning identification of EEG features predicting working memory performance in schizophrenia and healthy adults.

ABSTRACT:

Background

With millisecond-level resolution, electroencephalographic (EEG) recording provides a sensitive tool to assay neural dynamics of human cognition. However, selection of EEG features used to answer experimental questions is typically determined a priori. The utility of machine learning was investigated as a computational framework for extracting the most relevant features from EEG data empirically.

Methods

Schizophrenia (SZ; n = 40) and healthy community (HC; n = 12) subjects completed a Sternberg Working Memory Task (SWMT) during EEG recording. EEG was analyzed to extract 5 frequency components (theta1, theta2, alpha, beta, gamma) at 4 processing stages (baseline, encoding, retention, retrieval) and 3 scalp sites (frontal-Fz, cent

SUBMITTER: Johannesen JK 

PROVIDER: S-EPMC4928381 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature