Level-Dependent Subcortical Electroencephalography Responses to Continuous Speech
Bibliographic record
Abstract
Abstract The auditory brainstem response (ABR) is a measure of subcortical activity in response to auditory stimuli. The wave V peak of the ABR depends on stimulus intensity level, and has been widely used for clinical hearing assessment. Conventional methods to estimate the ABR average electroencephalography (EEG) responses to short unnatural stimuli such as clicks. Recent work has moved towards more ecologically relevant continuous speech stimuli using linear deconvolution models called Temporal Response Functions (TRFs). Investigating whether the TRF waveform changes with stimulus intensity is a crucial step towards the use of natural speech stimuli for hearing assessments involving subcortical responses. Here, we develop methods to estimate level-dependent subcortical TRFs using EEG data collected from 21 participants listening to continuous speech presented at 4 different intensity levels. We find that level-dependent changes can be detected in the wave V peak of the subcortical TRF for almost all participants, and are consistent with level-dependent changes in click-ABR wave V. We also investigate the most suitable peripheral auditory model to generate predictors for level-dependent subcortical TRFs and find that simple gammatone filterbanks perform the best. Additionally, around 6 minutes of data may be sufficient for detecting level-dependent effects and wave V peaks above the noise floor for speech segments with higher intensity. Finally, we show a proof-of-concept that level dependent subcortical TRFs can be detected even for the inherent intensity fluctuations in natural continuous speech. Visual abstract Significance statement Subcortical EEG responses to sound depend on the stimulus intensity level and provide a window into the early human auditory pathway. However, current methods detect responses using unnatural transient stimuli such as clicks or chirps. We develop methods for detecting level-dependent responses to continuous speech stimuli, which is more ecologically relevant and may provide several advantages over transient stimuli. Critically, we find consistent patterns of level dependent subcortical responses to continuous speech at an individual level, that are directly comparable to those seen for conventional responses to click stimuli. Our work lays the foundation for the use of subcortical responses to natural speech stimuli in future applications such as clinical hearing assessment and hearing assistive technology.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".