Speech processing is built upon encoding by the auditory nerve and brainstem, yet we know very little about how these processes unfold in specific subcortical structures. These structures are deep and respond quickly, making them difficult to study during ongoing speech. Recent techniques begin to address this problem, but yield temporally broad responses with consequently ambiguous neural origins. Here we describe a method that pairs re-synthesized 'peaky' speech with deconvolution analysis of EEG recordings. We show that in adults with normal hearing, the method quickly yields robust responses whose component waves reflect activity from distinct subcortical structures spanning auditory nerve to rostral brainstem. We further demonstrate the versatility of peaky speech by simultaneously measuring bilateral and ear-specific responses across different frequency bands, and discuss important practical considerations such as talker choice. The peaky speech method holds promise as a tool for investigating speech encoding and processing, and for clinical applications.
Python code is available on the lab GitHub account (https://github.com/maddoxlab/peaky-speech). All EEG recordings are posted in the EEG-BIDS format (Pernet et al., 2019) to Dryad (https://doi.org/10.5061/dryad.12jm63xwd). Stimulus files necessary to derive the peaky speech responses are deposited in the same Dryad repository.
Exposing distinct subcortical components of the auditory brainstem response evoked by continuous naturalistic speechDryad Digital Repository, doi:10.5061/dryad.12jm63xwd.
- Ross K Maddox
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Human subjects: All subjects gave written informed consent before the experiment began. All experimental procedures were approved by the University of Rochester Research Subjects Review Board. (#1227).
- Tobias Reichenbach, Imperial College London, United Kingdom
© 2021, Polonenko & Maddox
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.