Nonlinearity Detection and Compensation for EEG-Based Speech Tracking

Johanna Wilroth; Emina Alickovic; Martin A. Skoglund; Martin Enqvist

ICASSP 2024accepted0 citations

Nonlinearity Detection and Compensation for EEG-Based Speech Tracking

Johanna Wilroth, Emina Alickovic, Martin A. Skoglund, Martin Enqvist

Abstract

Clusters of neurons generate electrical signals which propagate in all directions through brain tissue, skull, and scalp of different conductivity. Measuring these signals with electroencephalography (EEG) sensors placed on the scalp results in noisy data. This can have severe impact on estimation, such as, source localization and temporal response functions (TRFs). We hypothesize that some of the noise is due to a Wiener-structured signal propagation with both linear and nonlinear components. We have developed a simple nonlinearity detection and compensation method for EEG data analysis and utilize a model for estimating source-level (SL) TRFs for evaluation. Our results indicate that the nonlinearity compensation method produce more precise and synchronized SL TRFs compared to the original EEG data.

BibTeX

@inproceedings{icassp2024_nonlinearitydete,
  title = {Nonlinearity Detection and Compensation for EEG-Based Speech Tracking},
  author = {Johanna Wilroth and Emina Alickovic and Martin A. Skoglund and Martin Enqvist},
  booktitle = {ICASSP 2024},
  year = {2024}
}