Evaluating the efficacy of fully automated approaches for the selection of eyeblink ICA components.

ABSTRACT: Independent component analysis (ICA) offers a powerful approach for the isolation and removal of eyeblink artifacts from EEG signals. Manual identification of the eyeblink ICA component by inspection of scalp map projections, however, is prone to error, particularly when nonartifactual components exhibit topographic distributions similar to the blink. The aim of the present investigation was to determine the extent to which automated approaches for selecting eyeblink-related ICA components could be utilized to replace manual selection. We evaluated popular blink selection methods relying on spatial features (EyeCatch), combined stereotypical spatial and temporal features (ADJUST), and a novel method relying on time series features alone (icablinkmetrics) using both simulated and real EEG d

SUBMITTER: Pontifex MB 

PROVIDER: S-EPMC5397386 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature