Beyond linear neural envelope tracking: a mutual information approach

Abstract

Abstract Objective. The human brain tracks the temporal envelope of speech, which contains essential cues for speech understanding. Linear models are the most common tool to study neural envelope tracking. However, information on how speech is processed can be lost since nonlinear relations are precluded. Analysis based on mutual information (MI), on the other hand, can detect both linear and nonlinear relations and is gradually becoming more popular in the field of neural envelope tracking. Yet, several different approaches to calculating MI are applied with no consensus on which approach to use. Furthermore, the added value of nonlinear techniques remains a subject of debate in the field. The present paper aims to resolve these open questions. Approach. We analyzed electroencephalography (EEG) data of participants listening to continuous speech and applied MI analyses and linear models. Main results. Comparing the different MI approaches, we conclude that results are most reliable and robust using the Gaussian copula approach, which first transforms the data to standard Gaussians. With this approach, the MI analysis is a valid technique for studying neural envelope tracking. Like linear models, it allows spatial and temporal interpretations of speech processing, peak latency analyses, and applications to multiple EEG channels combined. In a final analysis, we tested whether nonlinear components were present in the neural response to the envelope by first removing all linear components in the data. We robustly detected nonlinear components on the single-subject level using the MI analysis. Significance. We demonstrate that the human brain processes speech in a nonlinear way. Unlike linear models, the MI analysis detects such nonlinear relations, proving its added value to neural envelope tracking. In addition, the MI analysis retains spatial and temporal characteristics of speech processing, an advantage lost when using more complex (nonlinear) deep neural networks.