Detecting cortical responses to continuous running speech using EEG data from only one channel

Abstract

Objective: To explore the detection of cortical responses to continuous speech using a single EEG channel. Particularly, to compare detection rates and times using a cross-correlation approach and parameters extracted from the temporal response function (TRF).

Design: EEG from 32-channels were recorded whilst presenting 25-min continuous English speech. Detection parameters were cross-correlation between speech and EEG (XCOR), peak value and power of the TRF filter (TRF-peak and TRF-power), and correlation between predicted TRF and true EEG (TRF-COR). A bootstrap analysis was used to determine response statistical significance. Different electrode configurations were compared: Using single channels Cz or Fz, or selecting channels with the highest correlation value.

Study sample: Seventeen native English-speaking subjects with mild-to-moderate hearing loss.

Results: Significant cortical responses were detected from all subjects at Fz channel with XCOR and TRFCOR. Lower detection time was seen for XCOR (mean ¼ 4.8 min) over TRF parameters (best TRF-COR, mean ¼ 6.4 min), with significant time differences from XCOR to TRF-peak and TRF-power. Analysing multiple EEG channels and testing channels with the highest correlation between envelope and EEG reduced detection sensitivity compared to Fz alone.

Conclusions: Cortical responses to continuous speech can be detected from a single channel with recording times that may be suitable for clinical application.

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Identifiers

ORCID for David Simpson: orcid.org/0000-0001-9072-5088

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