Influence of auditory attention on sentence recognition captured by the neural phase

Müller, Jana; Kollmeier, Birger; Debener, Stefan; Brand, Thomas

doi:10.1111/ejn.13896

Cited by 3 publications

(3 citation statements)

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“…Poor spatial segregation in EEG is typically seen for auditory evoked cortical responses such as N1 and P2 components (Pereira et al, 2014) . It is also the case of tracking at theta frequencies where EEG scalp distribution of coherence maximum at the vertex and at 33/48 bilateral temporo-occipital electrodes is compatible with N1 topography, as also evident in previous studies (Müller et al, 2018;Ng et al, 2013) .…”

Section: Effect Of Added Auditory Noise On Speech Brain Trackingsupporting

confidence: 83%

“…Still, the neural mechanisms allowing the human brain to decode speech signals in real time are poorly understood . Based on electroencephalographic (EEG) or magnetoencephalographic (MEG) recordings, it was shown that listerner's auditory cortices track the time course of speaker's speech temporal envelope at 0.5 Hz (delta frequencies) and 4-8 Hz (theta frequencies) (Ahissar et al, 2001;Bourguignon et al, 2013;Broderick et al, 2017;Di Liberto et al, 2015;Ding et al, 2017;Ding and Simon, 2014;Gross et al, 2013;Horton et al, 2013;Keitel et al, 2018;Kösem and van Wassenhove, 2016;Luo and Poeppel, 2007;Meyer and Gumbert, 2018;Molinaro et al, 2016;Müller et al, 2018;O'Sullivan et al, 2014;Peelle et al, 2013;Pellegrino et al, 2011;Puschmann et al, 2017; . As delta and theta frequencies match with phrasal/sentence and syllable repetition rates respectively, it has been hypothesized that corresponding brain oscillations subserve the chunking of incoming speech into relevant segments for further speech recognition (Ahissar et al, 2001) .…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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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.

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Section: Effect Of Added Auditory Noise On Speech Brain Trackingsupporting

confidence: 83%

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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“…Thus, neural oscillations shape speech perception (e.g., Bosker and Ghitza, 2018). Many studies demonstrated that selective attention modulates neural entrainment and leads to a selectively enhanced representation of the attended stream (e.g., O’Sullivan et al, 2014; Mirkovic et al, 2015, 2016; Petersen et al, 2017; Müller et al, 2018). For instance, Petersen et al (2017) presented continuous speech either in quiet or masked by a competing talker at different signal-to-noise ratios (SNRs) to participants with hearing impairment and investigated the influence of hearing loss, SNR, and attention on neural tracking of speech.…”

Section: Introductionmentioning

confidence: 99%

Effect of Speech Rate on Neural Tracking of Speech

et al. 2019

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Speech comprehension requires effort in demanding listening situations. Selective attention may be required for focusing on a specific talker in a multi-talker environment, may enhance effort by requiring additional cognitive resources, and is known to enhance the neural representation of the attended talker in the listener’s neural response. The aim of the study was to investigate the relation of listening effort, as quantified by subjective effort ratings and pupil dilation, and neural speech tracking during sentence recognition. Task demands were varied using sentences with varying levels of linguistic complexity and using two different speech rates in a picture-matching paradigm with 20 normal-hearing listeners. The participants’ task was to match the acoustically presented sentence with a picture presented before the acoustic stimulus. Afterwards they rated their perceived effort on a categorical effort scale. During each trial, pupil dilation (as an indicator of listening effort) and electroencephalogram (as an indicator of neural speech tracking) were recorded. Neither measure was significantly affected by linguistic complexity. However, speech rate showed a strong influence on subjectively rated effort, pupil dilation, and neural tracking. The neural tracking analysis revealed a shorter latency for faster sentences, which may reflect a neural adaptation to the rate of the input. No relation was found between neural tracking and listening effort, even though both measures were clearly influenced by speech rate. This is probably due to factors that influence both measures differently. Consequently, the amount of listening effort is not clearly represented in the neural tracking.

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