Improving emotion perception in cochlear implant users: insights from machine learning analysis of EEG signals
Bibliographic record
Abstract
BACKGROUND: Although cochlear implants can restore auditory inputs to deafferented auditory cortices, the quality of the sound signal transmitted to the brain is severely degraded, limiting functional outcomes in terms of speech perception and emotion perception. The latter deficit negatively impacts cochlear implant users' social integration and quality of life; however, emotion perception is not currently part of rehabilitation. Developing rehabilitation programs incorporating emotional cognition requires a deeper understanding of cochlear implant users' residual emotion perception abilities. METHODS: To identify the neural underpinnings of these residual abilities, we investigated whether machine learning techniques could be used to identify emotion-specific patterns of neural activity in cochlear implant users. Using existing electroencephalography data from 22 cochlear implant users, we employed a random forest classifier to establish if we could model and subsequently predict from participants' brain responses the auditory emotions (vocal and musical) presented to them. RESULTS: Our findings suggest that consistent emotion-specific biomarkers exist in cochlear implant users, which could be used to develop effective rehabilitation programs incorporating emotion perception training. CONCLUSIONS: This study highlights the potential of machine learning techniques to improve outcomes for cochlear implant users, particularly in terms of emotion perception.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".