Speech Intelligibility Predicted from Neural Entrainment of the Speech Envelope

Vanthornhout, Jonas; Decruy, Lien; Wouters, Jan; Simon, Jonathan Z.; Francart, Tom

doi:10.1007/s10162-018-0654-z

Cited by 220 publications

(286 citation statements)

References 30 publications

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“…The resulting signals from all subbands were then summed, after which the signal was downsampled to 32 Hz and filtered using the same 1-9 Hz bandpass filter as for the EEG signal, which then resulted in a smooth envelope. We decided to keep the preprocessing identical to the original paper Das et al (2016) as it is application specific, and hence has differences with the preprocessing done in section III-A2 (based on Vanthornhout et al (2018)). The EEG data was referenced to the Cz electrode 2 (therefore, C = 63).…”

Section: Validation Experimentsmentioning

confidence: 99%

“…2) Dataset II: This dataset consists of EEG data from Vanthornhout et al (2018). In this study the EEG (64 channels sampled at 8192 Hz) was recorded from 27 normal hearing subjects (8 male, 19 female, average age: 23 years).…”

Section: Validation Experimentsmentioning

confidence: 99%

“…Preprocessing of the EEG data was done similar to Vanthornhout et al (2018). The EEG was highpass filtered (second order Butterworth with cut-off at 0.5 Hz) and downsampled to 256 Hz before applying the MWF for artifact rejection (Somers et al, 2018).…”

Section: Validation Experimentsmentioning

confidence: 99%

“…The correlations between the original neural responses and the predicted stimulus following responses are analyzed (Aiken and Picton, 2008;Di Liberto et al, 2015). The analysis of these correlations can not only contribute to advancing our knowledge of how the brain responds to auditory stimuli under different conditions, but also has potential to act as objective measures of speech intelligibility (Broderick et al, 2018;Di Liberto et al, 2018;Ding and Simon, 2012a;Lesenfants et al, 2019;Vanthornhout et al, 2018).…”

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confidence: 99%

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Stimulus-aware spatial filtering for single-trial neural response and temporal response function estimation in high-density EEG with applications in auditory research

Das

Vanthornhout

Francart

et al. 2019

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A common problem in neural recordings is the low signal-to-noise ratio (SNR), particularly when using noninvasive techniques like magneto-or electroencephalography (M/EEG). To address this problem, experimental designs often include repeated trials, which are then averaged to improve the SNR or to inform the design of a spatial filter that projects the data onto high-SNR directions. However, collecting enough repeated trials is often impractical and even impossible in some paradigms. Therefore, we present a data-driven spatial filter design that takes advantage of the knowledge of the presented stimulus, to achieve a joint noise reduction and dimensionality reduction without the need for repeated trials. The method uses the stimulus-driven neural response, which is then used to find a set of spatial filters that maximize the SNR based on a generalized eigenvalue decomposition. As the method is fully data-driven, the dimensionality reduction enables researchers to perform their analyses without having to rely on their knowledge of brain regions of interest, which increases accuracy and reduces the human factor in the results. In the context of neural tracking of a speech stimulus, our method resulted in better short-term temporal response function (TRF) estimates, higher correlations between predicted and actual neural responses, and higher attention decoding accuracies compared to existing TRF-based decoding methods. We also provide an extensive discussion on the central role played by the generalized eigenvalue decomposition in various denoising methods in the literature, and address the conceptual similarities and differences with our proposed method.

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Section: Validation Experimentsmentioning

confidence: 99%

Section: Validation Experimentsmentioning

confidence: 99%

Section: Validation Experimentsmentioning

confidence: 99%

mentioning

confidence: 99%

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Stimulus-aware spatial filtering for single-trial neural response and temporal response function estimation in high-density EEG with applications in auditory research

Das

Vanthornhout

Francart

et al. 2019

Preprint

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“…The correlation between the reconstructed/predicted artificial time-course and the actual values is then a measure of cortical speech tracking. Measures of cortical speech tracking quantify speech processing within the brain opening doors to, for example, an objective measure of an individual speech understanding [4][5][6][7] or auditory attention decoding in a cocktail party scenario [8][9][10] .…”

Section: Introductionmentioning

confidence: 99%

The interplay of top-down focal attention and the cortical tracking of speech

Lesenfants

Francart

2019

Preprint

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AbstractMany active neuroimaging paradigms rely on the assumption that the participant sustains attention to a task. However, in practice, there will be momentary distractions, potentially influencing the results. We investigated the effect of focal attention, objectively quantified using a measure of brain signal entropy, on cortical tracking of the speech envelope. The latter is a measure of neural processing of naturalistic speech. We let participants listen to 44 minutes of natural speech, while their electroencephalogram was recorded, and quantified both entropy and cortical envelope tracking. Focal attention affected the later brain responses to speech, between 100 and 300 ms latency. By only taking into account periods with higher attention, the measured cortical speech tracking improved by 47%. This illustrates the impact of the participant’s active engagement in the modeling of the brain-speech response and the importance of accounting for it. Our results suggests a cortico-cortical loop that initiates during the early-stages of the auditory processing, then propagates through the parieto-occipital and frontal areas, and finally impacts the later-latency auditory processes in a top-down fashion. The proposed framework could be transposed to other active electrophysiological paradigms (visual, somatosensory, etc) and help to control the impact of participants’ engagement on the results.

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