3 research outputs found
Group-Level EEG-Processing Pipeline for Flexible Single Trial-Based Analyses Including Linear Mixed Models
Here we present an application of an EEG processing pipeline customizing EEGLAB and
FieldTrip functions, specifically optimized to flexibly analyze EEG data based on single
trial information. The key component of our approach is to create a comprehensive
3-D EEG data structure including all trials and all participants maintaining the original
order of recording. This allows straightforward access to subsets of the data based
on any information available in a behavioral data structure matched with the EEG data
(experimental conditions, but also performance indicators, such accuracy or RTs of
single trials). In the present study we exploit this structure to compute linear mixed
models (LMMs, using lmer in R) including random intercepts and slopes for items. This
information can easily be read out fromthematched behavioral data, whereas itmight not
be accessible in traditional ERP approaches without substantial effort.We further provide
easily adaptable scripts for performing cluster-based permutation tests (as implemented
in FieldTrip), as a more robust alternative to traditional omnibus ANOVAs. Our approach
is particularly advantageous for data with parametric within-subject covariates (e.g.,
performance) and/or multiple complex stimuli (such as words, faces or objects) that vary
in features affecting cognitive processes and ERPs (such as word frequency, salience
or familiarity), which are sometimes hard to control experimentally or might themselves
constitute variables of interest. The present dataset was recorded from 40 participants
who performed a visual search task on previously unfamiliar objects, presented either
visually intact or blurred. MATLAB as well as R scripts are provided that can be adapted
to different datasets.Peer Reviewe