ArticleBMC neurology2024
Improving emotion perception in cochlear implant users: insights from machine learning analysis of EEG signals.
Article in BMC neurology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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Who cites it
3 citing papers in PubMed.
- Prediction of Auditory Performance in Cochlear Implants Using Machine Learning Methods: A Systematic Review.Audiology research · 2025Review
- Artificial intelligence-enabled innovations in cochlear implant technology: Advancing auditory prosthetics for hearing restoration.Bioengineering & translational medicine · 2025Review
- Vocal Emotion Perception and Musicality-Insights from EEG Decoding.Sensors (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAlthough 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.
methodsTo 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.
resultsOur 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.
conclusionsThis study highlights the potential of machine learning techniques to improve outcomes for cochlear implant users, particularly in terms of emotion perception.
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