Cross-Species Word Recognition Using SVM-Based Cochlear Implant Coding Strategies
Session Number
Project ID: CMPS 26
Advisor(s)
Dr. Claus-Peter Richter, Northwestern University, Feinberg School of Medicine
Discipline
Computer Science
Start Date
17-4-2024 8:55 AM
End Date
17-4-2024 9:10 AM
Abstract
This study investigates the feasibility of using support vector machine (SVM) algorithms and other advanced classification techniques to decode neural responses from the guinea pig auditory system (inferior colliculus) while aiming to discern a spoken word processed by the animal. Utilizing multi-channel electrodes to record neural response patterns from the inferior colliculus of four guinea pigs, we develop SVM models to infer the uttered words. While our results are preliminary auditory signals. Integrating insights from this approach could inform the optimization of cochlear implant coding strategies for human recipients with hearing disorders. Future research endeavors will further refine and validate these SVM models and collect more neural response data from the guinea pigs, which will, indicating ongoing experimentation, initial findings suggest potential for SVM-based decoding of guinea pig ultimately contribute to advancements in cross-species neural communication and auditory prosthetic technologies.
Cross-Species Word Recognition Using SVM-Based Cochlear Implant Coding Strategies
This study investigates the feasibility of using support vector machine (SVM) algorithms and other advanced classification techniques to decode neural responses from the guinea pig auditory system (inferior colliculus) while aiming to discern a spoken word processed by the animal. Utilizing multi-channel electrodes to record neural response patterns from the inferior colliculus of four guinea pigs, we develop SVM models to infer the uttered words. While our results are preliminary auditory signals. Integrating insights from this approach could inform the optimization of cochlear implant coding strategies for human recipients with hearing disorders. Future research endeavors will further refine and validate these SVM models and collect more neural response data from the guinea pigs, which will, indicating ongoing experimentation, initial findings suggest potential for SVM-based decoding of guinea pig ultimately contribute to advancements in cross-species neural communication and auditory prosthetic technologies.