Effects of Spectral Degradation on Attentional Modulation of Cortical Auditory Responses to Continuous Speech

Kong, Ying‐Yee; Somarowthu, Ala; Ding, Nai

doi:10.1007/s10162-015-0540-x

Cited by 51 publications

(56 citation statements)

References 46 publications

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“…Another hypothesis assumes that envelope tracking responses reflect interactions between auditory and language processing. Consistent with this hypothesis, some studies show that when speech is acoustically degraded to compromise intelligibility, neural tracking of the speech envelope shows reduced precision Kong et al, 2015;Luo and Poeppel, 2007;Peelle et al, 2013). Furthermore, at the individual level, listeners 5 showing more precise envelope tracking activity tend to understand speech better Ding and Simon, 2013;Doelling et al, 2014).…”

Section: Introductionmentioning

confidence: 72%

Auditory and Language Contributions to Neural Encoding of Speech Features in Noisy Environments

Zou

Feng

et al. 2018

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Recognizing speech in noisy environments is a challenging task that involves both auditory and language mechanisms. Previous studies have demonstrated noise-robust neural tracking of the speech envelope, i.e., fluctuations in sound intensity, in human auditory cortex, which provides a plausible neural basis for noise-robust speech recognition. The current study aims at teasing apart auditory and language contributions to noise-robust envelope tracking by comparing 2 groups of listeners, i.e., native listeners of the testing language and foreign listeners who do not understand the testing language. In the experiment, speech is mixed with spectrally matched stationary noise at 4 intensity levels and the neural responses are recorded using electroencephalography (EEG). When the noise intensity increases, an increase in neural response gain is observed for both groups of listeners, demonstrating auditory gain control mechanisms. Language comprehension creates no overall boost in the response gain or the envelope-tracking precision but instead modulates the spatial and temporal profiles of envelopetracking activity. Based on the spatio-temporal dynamics of envelope-tracking activity, the 2 groups of listeners and the 4 levels of noise intensity can be jointly decoded by a linear classifier. All together, the results show that without feedback from language processing, auditory mechanisms such as gain control can lead to a noise-robust speech representation. High-level language processing, however, further modulates the spatial-temporal profiles of the neural representation of the speech envelope.

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Section: Introductionmentioning

confidence: 72%

Auditory and Language Contributions to Neural Encoding of Speech Features in Noisy Environments

Zou

Feng

et al. 2018

Preprint

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“…Here, the stimulus does not have to be known, but a major practical limitation comes with the condition that it can only be performed if there are enough repetitions of the same stimulus in the data to effectively enhance the stimulus following responses by averaging. DSS is often used to denoise MEG responses with a relatively small number of repetitions (Akram et al, 2016;Ding and Simon, 2012b;Miran et al, 2018), but the SNR in EEG responses is considerably lower (Kong et al, 2015). In addition, the GEVD approach renders pre-whitening and the PCA steps unnecessary, resulting in improved computational efficiency.…”

Section: Discussionmentioning

confidence: 99%

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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“…Selecting one auditory stream and ignoring another could therefore involve enhancing neural entrainment to the rhythmic patterns in the target stream, and diminishing entrainment to the patterns in the distractor stream (25). Indeed, when multiple speech streams are presented, neural entrainment to the temporal envelope of both the target and the background is present, but that of the target speaker is enhanced relative to the distractor speakers (15,(26)(27)(28)(29), and the extent of this modulation correlates with speech comprehension scores (30). Moreover, transcranial stimulation with currents shaped similarly to the amplitude envelope of target speech can facilitate speech recognition performance in cocktail party paradigms and fMRI responses to speech (24,31), demonstrating that neural entrainment plays a causal role in auditory object selection.…”

Section: Introductionmentioning

confidence: 99%

Attentional modulation of neural phase is enhanced by short-term training and linked to musical experience

Laffere

Dick

Tierney

2019

Preprint

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How does the brain follow a sound that is mixed with others in a noisy environment? A possible strategy is to allocate attention to task-relevant time intervals while suppressing irrelevant intervals -a strategy that could be implemented by aligning neural modulations with critical moments in time. Here we tested whether selective attention to non-verbal sound streams is linked to shifts in the timing of attentional modulations of EEG activity, and investigated whether this neural mechanism can be enhanced by short-term training and musical experience. Participants performed a memory task on a target auditory stream presented at 4 Hz while ignoring a distractor auditory stream also presented at 4 Hz, but with a 180-degree shift in phase. The two attention conditions were linked to a roughly 180-degree shift in phase in the EEG signal at 4 Hz. Moreover, there was a strong relationship between performance on the 1-back task and the timing of the EEG modulation with respect to the attended band. EEG modulation timing was also enhanced after several days of training on the selective attention task and enhanced in experienced musicians. These results support the hypothesis that modulation of neural timing facilitates attention to particular moments in time and indicate that phase timing is a robust and reliable marker of individual differences in auditory attention. Moreover, these results suggest that nonverbal selective attention can be enhanced in the short term by only a few hours of practice and in the long term by years of musical training.

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Effects of Spectral Degradation on Attentional Modulation of Cortical Auditory Responses to Continuous Speech

Cited by 51 publications

References 46 publications

Auditory and Language Contributions to Neural Encoding of Speech Features in Noisy Environments

Auditory and Language Contributions to Neural Encoding of Speech Features in Noisy Environments

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

Attentional modulation of neural phase is enhanced by short-term training and linked to musical experience

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