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A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

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Beyond linear neural envelope tracking: a mutual information approach.

Pieter De Clercq¹, Jonas Vanthornhout¹, Maaike Vandermosten¹

¹Experimental Oto-Rhino-Laryngology, Department of Neurosciences, Leuven Brain Institute, KU Leuven, Belgium.

Journal of Neural Engineering

|February 22, 2023

Summary

The human brain processes speech nonlinearly, a finding revealed by mutual information (MI) analysis. This method offers advantages over linear models for studying neural envelope tracking and speech comprehension.

Keywords:

auditory speech processing mutual information neural envelope tracking

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Area of Science:

Neuroscience
Cognitive Science
Signal Processing

Background:

The human brain processes the temporal envelope of speech for understanding.
Linear models are common for studying neural envelope tracking but miss nonlinearities.
Mutual Information (MI) analysis can detect linear and nonlinear relations, but consensus on methods is lacking.

Purpose of the Study:

To resolve debates on the added value of nonlinear techniques in neural envelope tracking.
To establish a reliable method for calculating MI in this context.
To demonstrate the presence of nonlinearities in neural speech processing.

Main Methods:

Analysis of electroencephalography (EEG) data from participants listening to continuous speech.
Application of various Mutual Information (MI) analyses and linear models.
Utilizing the Gaussian copula approach for MI calculation by transforming data to standard Gaussians.

Main Results:

The Gaussian copula approach provides the most reliable and robust MI results for neural envelope tracking.
MI analysis, like linear models, allows for spatial, temporal, and peak latency interpretations.
Nonlinear components in the neural response to speech envelopes were robustly detected after removing linear components.

Conclusions:

The human brain exhibits nonlinear processing of speech.
MI analysis is a valuable technique for neural envelope tracking, surpassing linear models by detecting nonlinearities.
MI analysis preserves crucial spatial and temporal characteristics of speech processing, unlike complex deep neural networks.