Eelbrain 0.32

et al. 2022

Eur J of Neuroscience

We investigated the impact of hearing loss on the neural processing of speech. Using a forward modelling approach, we compared the neural responses to continuous speech of 14 adults with sensorineural hearing loss with those of age‐matched normal‐hearing peers. Compared with their normal‐hearing peers, hearing‐impaired listeners had increased neural tracking and delayed neural responses to continuous speech in quiet. The latency also increased with the degree of hearing loss. As speech understanding decreased, neural tracking decreased in both populations; however, a significantly different trend was observed for the latency of the neural responses. For normal‐hearing listeners, the latency increased with increasing background noise level. However, for hearing‐impaired listeners, this increase was not observed. Our results support the idea that the neural response latency indicates the efficiency of neural speech processing: More or different brain regions are involved in processing speech, which causes longer communication pathways in the brain. These longer communication pathways hamper the information integration among these brain regions, reflected in longer processing times. Altogether, this suggests decreased neural speech processing efficiency in HI listeners as more time and more or different brain regions are required to process speech. Our results suggest that this reduction in neural speech processing efficiency occurs gradually as hearing deteriorates. From our results, it is apparent that sound amplification does not solve hearing loss. Even when listening to speech in silence at a comfortable loudness, hearing‐impaired listeners process speech less efficiently.

Section: Discussionmentioning

confidence: 99%

Section: Prediction Accuracies Trf and Peakpicking Methodsmentioning

confidence: 99%

Hearing loss is associated with delayed neural responses to continuous speech

Gillis

Decruy

et al. 2022

Eur J of Neuroscience

“…This prediction accuracy is a measure of neural tracking. We used the boosting algorithm (David et al, 2007) implemented by the Eelbrain Toolbox (Brodbeck, 2020) to estimate the TRF and obtain the prediction accuracy. We used an integration window of -100 to 600 ms, i.e., the neural response is estimated ranging from 100 ms before activation of the stimulus characteristic to 600 ms after its activation.…”

Section: Determination Of Neural Trackingmentioning

confidence: 99%

“…This prediction accuracy is a measure of neural tracking. We used the boosting algorithm (David et al, 2007) implemented by the Eelbrain Toolbox (Brodbeck, 2020) to estimate the TRF and obtain the prediction accuracy. The boosting algorithm estimates the TRF in a sparse and iterative way.…”

Section: Determination Of Neural Trackingmentioning

confidence: 99%

Speech understanding oppositely affects acoustic and linguistic neural tracking in a speech rate manipulation paradigm

Verschueren

Gillis

et al. 2022

Preprint

When listening to continuous speech, the human brain can track features of the presented speech signal. It has been shown that neural tracking of acoustic features is a prerequisite for speech understanding and can predict speech understanding in controlled circumstances. However, the brain also tracks linguistic features of speech, which may be more directly related to speech understanding. We investigated acoustic and linguistic speech processing as a function of varying speech understanding by manipulating the speech rate. In this paradigm, acoustic and linguistic speech processing are affected simultaneously but in opposite directions: When the speech rate increases, more acoustic information per second is present. In contrast, linguistic information decreases as speech becomes less intelligible at higher speech rates. We measured the EEG of 18 participants who listened to speech at various speech rates. As expected and confirmed by the behavioral results, speech understanding decreased with increasing speech rate. Accordingly, linguistic neural tracking decreased with increasing speech rate, but acoustic neural tracking increased. This indicates that neural tracking of linguistic representations can capture the gradual effect of decreasing speech understanding. In addition, increased acoustic neural tracking does not necessarily imply better speech understanding. This suggests that, although more challenging to measure due to the low signal-to-noise ratio, linguistic neural tracking may be a more direct predictor of speech understanding.

“…This approach estimates the Temporal Response Function (TRF), a kernel that describes the neural response at different latencies to the speech envelope. We used the Eelbrain toolbox (Brodbeck, 2020) to estimate the TRF for each EEG channel separately using the boosting algorithm (David et al, 2007). To avoid overfitting, a 10-fold cross-validation was used (10 equally long folds, 8 for training, 1 for validation and 1 for testing).…”

Section: Linear Forward Modelmentioning

confidence: 99%

Beyond Linear Neural Envelope Tracking: A Mutual Information Approach

Clercq

Vandermosten

et al. 2022

Preprint

The human brain tracks the temporal envelope of speech, which contains essential cues for speech understanding. Linear models are the most common tool to study neural envelope tracking. However, information on how speech is processed can be lost since nonlinear relations are precluded. As an alternative, mutual information (MI) analysis can detect both linear and nonlinear relations. Yet, several different approaches to calculating MI are applied without consensus on which approach to use. Furthermore, the added value of nonlinear techniques remains a subject of debate in the field. To resolve this, we applied linear and MI analyses to electroencephalography (EEG) data of participants listening to continuous speech. Comparing the different MI approaches, we conclude that results are most reliable and robust using the Gaussian copula approach, which first transforms the data to standard Gaussians. With this approach, the MI analysis is a valid technique for studying neural envelope tracking. Like linear models, it allows spatial and temporal interpretations of speech processing, peak latency analyses, and applications to multiple EEG channels combined. Finally, we demonstrate that the MI analysis can detect nonlinear components on the single-subject level, beyond the limits of linear models. We conclude that the MI analysis is a more informative tool for studying neural envelope tracking.