Yifan Wang (ESR 2)
Abstract (pdf)
Poster (pdf)
ESR2_WS2_poster_updated
Click on the image to join the Live on December 13, from 12:30 to 14:30. You can also leave a comment at the bottom of the page or send an email to Yifan: wang.yifan@hoersys.de
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- Biological diversity in plasticity and adaptation
- Multimodality and optimisation of cognitive resources
- 5. Temporal course of auditory, labial, and manual signals in Cued Speech perception
- 6. Exploiting the somatosensory function in hearing impaired subjects for understanding and rehabilitating the perceptuo-motor loop in speech communication
- 7. Improving integration of audio-visual speech cues in children with hearing impairment
- 8. Effortful listening and auditory learning in children with hearing impairment
- 9. Theory of mind, language and cognitive skills in deaf and deafblind children (Usher syndrome) fitted with cochlear implants
- 10. Automatic recognition and generation of Cued Speech using deep learning
- Environment and enhancement of language skills
- 11. The acquisition of multiple spoken languages in deaf children with cochlear implants who grow up in plurilingual contexts
- 12. Multimodal communication among deaf and hearing interactants in the presence of sensory and communication asymmetries
- 13. Spelling ability and associated skills
- 14. Sustaining oral communicative skills in children with hearing impairment
- 15. The phonological body – body movements to accompany linguistic development in deaf children
- People
- Publications
- Comm4CHILD
- Conference
- Fact Sheet 2024
- Policy Brief
- Beneficiaries
- Partners
- Research projects
- Biological diversity in plasticity and adaptation
- Multimodality and optimisation of cognitive resources
- 5. Temporal course of auditory, labial, and manual signals in Cued Speech perception
- 6. Exploiting the somatosensory function in hearing impaired subjects for understanding and rehabilitating the perceptuo-motor loop in speech communication
- 7. Improving integration of audio-visual speech cues in children with hearing impairment
- 8. Effortful listening and auditory learning in children with hearing impairment
- 9. Theory of mind, language and cognitive skills in deaf and deafblind children (Usher syndrome) fitted with cochlear implants
- 10. Automatic recognition and generation of Cued Speech using deep learning
- Environment and enhancement of language skills
- 11. The acquisition of multiple spoken languages in deaf children with cochlear implants who grow up in plurilingual contexts
- 12. Multimodal communication among deaf and hearing interactants in the presence of sensory and communication asymmetries
- 13. Spelling ability and associated skills
- 14. Sustaining oral communicative skills in children with hearing impairment
- 15. The phonological body – body movements to accompany linguistic development in deaf children
- People
- Publications
Data analysis, modeling, and prediction of underperforming cochlear implant patients based on individual anatomy (CT), brain signals (EEG), and audiological tests
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Colibri
Hello Yifan
I’m soryy I didn’t get the time to visit your poster this afternnon. As far as I understood, your fisrt step was to work on a database. What is the next step ? and how will you assess CI outcomes (audiological tests, language tests, …) ? Best regards
Dear Cécile,
Thanks a lot for your comment.
It’s totally right we focus firstly on the database analysis( CT scan), then we will combine this part to compare with the CI outcomes.
In fact, now the result we have is just a first step of analysing the database. There are still some steps to go to get a final result from the database analysis.
Then regards the hearing tests for CI users, we haven’t decide the details for this part, but we think it will firstly include the speech perception test ( e.g. with a German HSM sentence test) , but also we may consider other hearing tests.
Best regards,
Yifan
A fascinating and highly important research initiative. Combining CT imaging, EEG brain signals, anatomical data, and audiological testing to better understand underperforming cochlear implant patients could significantly improve personalized treatment strategies. Predictive modeling in this area has the potential to transform post-implant rehabilitation and patient outcomes. Very promising interdisciplinary work.