Literature DB >> 30205208

Comparing the potential of MEG and EEG to uncover brain tracking of speech temporal envelope.

Florian Destoky1, Morgane Philippe2, Julie Bertels3, Marie Verhasselt2, Nicolas Coquelet2, Marc Vander Ghinst2, Vincent Wens4, Xavier De Tiège4, Mathieu Bourguignon5.   

Abstract

During connected speech listening, brain activity tracks speech rhythmicity at delta (∼0.5 Hz) and theta (4-8 Hz) frequencies. Here, we compared the potential of magnetoencephalography (MEG) and high-density electroencephalography (EEG) to uncover such speech brain tracking. Ten healthy right-handed adults listened to two different 5-min audio recordings, either without noise or mixed with a cocktail-party noise of equal loudness. Their brain activity was simultaneously recorded with MEG and EEG. We quantified speech brain tracking channel-by-channel using coherence, and with all channels at once by speech temporal envelope reconstruction accuracy. In both conditions, speech brain tracking was significant at delta and theta frequencies and peaked in the temporal regions with both modalities (MEG and EEG). However, in the absence of noise, speech brain tracking estimated from MEG data was significantly higher than that obtained from EEG. Furthemore, to uncover significant speech brain tracking, recordings needed to be ∼3 times longer in EEG than MEG, depending on the frequency considered (delta or theta) and the estimation method. In the presence of noise, both EEG and MEG recordings replicated the previous finding that speech brain tracking at delta frequencies is stronger with attended speech (i.e., the sound subjects are attending to) than with the global sound (i.e., the attended speech and the noise combined). Other previously reported MEG findings were replicated based on MEG but not EEG recordings: 1) speech brain tracking at theta frequencies is stronger with attended speech than with the global sound, 2) speech brain tracking at delta frequencies is stronger in noiseless than noisy conditions, and 3) when noise is added, speech brain tracking at delta frequencies dampens less in the left hemisphere than in the right hemisphere. Finally, sources of speech brain tracking reconstructed from EEG data were systematically deeper and more posterior than those derived from MEG. The present study demonstrates that speech brain tracking is better seen with MEG than EEG. Quantitatively, EEG recordings need to be ∼3 times longer than MEG recordings to uncover significant speech brain tracking. As a consequence, MEG appears more suited than EEG to pinpoint subtle effects related to speech brain tracking in a given recording time.
Copyright © 2018 Elsevier Inc. All rights reserved.

Keywords:  Coherence; EEG; MEG; Reconstruction accuracy; Speech brain tracking

Mesh:

Year:  2018        PMID: 30205208     DOI: 10.1016/j.neuroimage.2018.09.006

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


  7 in total

1.  Cortical Tracking of Speech-in-Noise Develops from Childhood to Adulthood.

Authors:  Marc Vander Ghinst; Mathieu Bourguignon; Maxime Niesen; Vincent Wens; Sergio Hassid; Georges Choufani; Veikko Jousmäki; Riitta Hari; Serge Goldman; Xavier De Tiège
Journal:  J Neurosci       Date:  2019-02-11       Impact factor: 6.167

2.  Cortical encoding of acoustic and linguistic rhythms in spoken narratives.

Authors:  Cheng Luo; Nai Ding
Journal:  Elife       Date:  2020-12-21       Impact factor: 8.140

3.  Dynamic Modulation of Beta Band Cortico-Muscular Coupling Induced by Audio-Visual Rhythms.

Authors:  Manuel Varlet; Sylvie Nozaradan; Laurel Trainor; Peter E Keller
Journal:  Cereb Cortex Commun       Date:  2020-08-05

4.  Reduced auditory steady state responses in autism spectrum disorder.

Authors:  R A Seymour; G Rippon; G Gooding-Williams; P F Sowman; K Kessler
Journal:  Mol Autism       Date:  2020-07-01       Impact factor: 7.509

5.  Simple Acoustic Features Can Explain Phoneme-Based Predictions of Cortical Responses to Speech.

Authors:  Christoph Daube; Robin A A Ince; Joachim Gross
Journal:  Curr Biol       Date:  2019-05-23       Impact factor: 10.834

6.  Defining Surgical Terminology and Risk for Brain Computer Interface Technologies.

Authors:  Eric C Leuthardt; Daniel W Moran; Tim R Mullen
Journal:  Front Neurosci       Date:  2021-03-26       Impact factor: 4.677

7.  Development of neural oscillatory activity in response to speech in children from 4 to 6 years old.

Authors:  Paula Ríos-López; Nicola Molinaro; Mathieu Bourguignon; Marie Lallier
Journal:  Dev Sci       Date:  2020-03-03
  7 in total

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