Cortical alpha oscillations in cochlear implant users reflect subjective listening effort during speech-in-noise perception
Bibliographic record
Abstract
Listening to speech in noise is effortful for individuals with hearing loss, even if they have received a hearing prosthesis such as a hearing aid or cochlear implant (CI). At present, little is known about the neural functions that support listening effort. One form of neural activity that has been suggested to reflect listening effort is the power of 8-12 Hz (alpha) oscillations measured by electroencephalography (EEG). Alpha power in two cortical regions has been associated with effortful listening-left inferior frontal gyrus (IFG), and parietal cortex-but these relationships have not been examined in the same listeners. Further, there are few studies available investigating neural correlates of effort in the individuals with cochlear implants. Here we tested 16 CI users in a novel effort-focused speech-in-noise listening paradigm, and confirm a relationship between alpha power and self-reported effort ratings in parietal regions, but not left IFG. The parietal relationship was not linear but quadratic, with alpha power comparatively lower when effort ratings were at the top and bottom of the effort scale, and higher when effort ratings were in the middle of the scale. Results are discussed in terms of cognitive systems that are engaged in difficult listening situations, and the implication for clinical translation.
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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".