Neural language tracking in audition is measured by either linear (cerebro-acoustic coherence) or nonlinear (Mutual Information) measures, but such techniques have yet to be compared. In this study, we plan to compare their sensitivity by analyzing a publicly available EEG dataset in English (Brennan, 2023) and collecting and analyzing comparable EEG data from native German speakers with English as a second language, using both German and English stimuli. Participants will listen to German and English audiobook versions of the first chapter of Alice in Wonderland (~12 minutes each, sequence counterbalanced), followed by eight comprehension questions. Brain activity will be recorded using a 64-active-electrode set-up. After EEG data preprocessing (filtering, Independent Component Analysis, normalization), we will calculate cerebro-acoustic coherence (constant lag between auditory stimuli and EEG data) and Mutual Information (Gaussian copula) testing native neural tracking effects across language (English native vs. German native), as well as the effect of nativeness per se (English native vs. English non-native).
References
Brennan, J. R. (2023). EEG datasets for naturalistic listening to "Alice in Wonderland"
(Version 2) [Data set]. University of Michigan - Deep Blue Data.
https://doi.org/10.7302/Z29C6VNH