Utilizing auditory emotion bio-markers, the underpinning of emotion perception improvement in cochlear implant users
Bibliographic record
Abstract
Cochlear implants (CI) have had tremendous success restoring a sense of hearing in the deaf. However, even after months of intensive rehabilitation, many CI users struggle with appreciating emotive tones in speech and music despite good speech comprehension. Failure to perceive emotional expression can result in maladjusted social behaviour, leading to detrimental socio-economic consequences. Recent advances in automated pattern identification of neuroimaging data can bring empirical support to developing training programs for emotion perception rehabilitation in CI users. We used a machine-learning approach to identify emotion-processing bio-markers in high-density electroencephalograms collected from CI users (22) and matched normal-hearing controls (22). Participants’ brain responses elicited by short musical and vocal emotional (happy, sad, and neutral) stimuli were used to train an algorithm to help identify, in each group, the pattern of brain responses that can best predict the presented emotion. Using this approach, we were able to confirm the presence of emotion-specific patterns of brain activity in CI users despite their reported emotion perception deficit. Identifying these patterns brings forward support for implementing a rehabilitation program for emotion perception for this population; if an algorithm can differentiate aurally presented emotions, perhaps CI users can learn to discriminate emotions.
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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) | 0.001 | 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".