A Deep Learning Based Approach In The Prediction Of Tinnitus Disease For Large Population Data
Author:
Affiliation:
1. SRM University AP,Department of Computer Science and Engineering,Amaravati,Andhra Pradesh,India
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10306338/10306339/10307000.pdf?arnumber=10307000
Reference19 articles.
1. Accounting for Heterogeneity: Mixed-Effects Models in Resting-State EEG Data in a Sample of Tinnitus Sufferers
2. Investigating the Efficacy of an Individualized Alpha/Delta Neurofeedback Protocol in the Treatment of Chronic Tinnitus;güntensperger;Neural Plast,2019
3. Heading for Personalized rTMS in Tinnitus: Reliability of Individualized Stimulation Protocols in Behavioral and Electrophysiological Responses;schoisswohl;Journal of Medicine and the Person,2021
4. Change in EEG Activity is Associated with a Decrease in Tinnitus Awareness after rTMS;carter;Frontiers of Neurology and Neuroscience,2021
5. Combining neurofeedback with source estimation: Evaluation of an sLORETA neurofeedback protocol for chronic tinnitus treatment
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