Single-trial log transformation is optimal in frequency analysis of resting EEG alpha.

ABSTRACT: The appropriate definition and scaling of the magnitude of electroencephalogram (EEG) oscillations is an underdeveloped area. The aim of this study was to optimize the analysis of resting EEG alpha magnitude, focusing on alpha peak frequency and nonlinear transformation of alpha power. A family of nonlinear transforms, Box-Cox transforms, were applied to find the transform that (a) maximized a non-disputed effect: the increase in alpha magnitude when the eyes are closed (Berger effect), and (b) made the distribution of alpha magnitude closest to normal across epochs within each participant, or across participants. The transformations were performed either at the single epoch level or at the epoch-average level. Alpha peak frequency showed large individual differences, yet good corresponden

SUBMITTER: Smulders FTY 

PROVIDER: S-EPMC6221126 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature