Improving emotion perception in cochlear implant users: insights from machine learning analysis of EEG signals
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 negativel...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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BMC
2024-04-01
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| Series: | BMC Neurology |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12883-024-03616-0 |
| _version_ | 1797209287838138368 |
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| author | Sebastien Paquette Samir Gouin Alexandre Lehmann |
| author_facet | Sebastien Paquette Samir Gouin Alexandre Lehmann |
| author_sort | Sebastien Paquette |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-24T09:52:18Z |
| format | Article |
| id | doaj.art-7ddb3da132bc4b6788bd3bd4b87ccc97 |
| institution | Directory Open Access Journal |
| issn | 1471-2377 |
| language | English |
| last_indexed | 2024-04-24T09:52:18Z |
| publishDate | 2024-04-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Neurology |
| spelling | doaj.art-7ddb3da132bc4b6788bd3bd4b87ccc972024-04-14T11:19:54ZengBMCBMC Neurology1471-23772024-04-012411510.1186/s12883-024-03616-0Improving emotion perception in cochlear implant users: insights from machine learning analysis of EEG signalsSebastien Paquette0Samir Gouin1Alexandre Lehmann2Psychology Department, Faculty of Arts and Science, Trent UniversityCentre for Research On Brain, Language, and Music (CRBLM), International Laboratory for Brain, Music & Sound Research (BRAMS), Psychology Department, University of MontrealResearch Institute of the McGill University Health Centre (RI-MUHC)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.https://doi.org/10.1186/s12883-024-03616-0Cochlear implantEmotion perceptionMachine learning |
| spellingShingle | Sebastien Paquette Samir Gouin Alexandre Lehmann Improving emotion perception in cochlear implant users: insights from machine learning analysis of EEG signals BMC Neurology Cochlear implant Emotion perception Machine learning |
| title | Improving emotion perception in cochlear implant users: insights from machine learning analysis of EEG signals |
| title_full | Improving emotion perception in cochlear implant users: insights from machine learning analysis of EEG signals |
| title_fullStr | Improving emotion perception in cochlear implant users: insights from machine learning analysis of EEG signals |
| title_full_unstemmed | Improving emotion perception in cochlear implant users: insights from machine learning analysis of EEG signals |
| title_short | Improving emotion perception in cochlear implant users: insights from machine learning analysis of EEG signals |
| title_sort | improving emotion perception in cochlear implant users insights from machine learning analysis of eeg signals |
| topic | Cochlear implant Emotion perception Machine learning |
| url | https://doi.org/10.1186/s12883-024-03616-0 |
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