Predicting Treatment Response to rTMS in Tinnitus Patients Using a Machine Learning Model Based on EEG and Clinical Features

Name: Predicting Treatment Response to rTMS in Tinnitus Patients Using a Machine Learning Model Based on EEG and Clinical Features
Creator: Zhongling Ding
Published: 2025-08-18T18:38:56.006Z
Keywords: Electroencephalography

Ding, Zhongling; Zhou, Wangcheng; Gong, Mengfang; Liu, Jisheng; Dai, Yakang; Tao, Duo-Duo

doi:10.17632/cj3j8cwnc8.1

Predicting Treatment Response to rTMS in Tinnitus Patients Using a Machine Learning Model Based on EEG and Clinical Features

Published: 18 August 2025| Version 1 | DOI: 10.17632/cj3j8cwnc8.1

Contributors:

Zhongling Ding, Wangcheng Zhou, Mengfang Gong, Jisheng Liu, Yakang Dai, Duo-Duo Tao

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The data supporting this study are available in the supplementary material file "Supplementary Table 1.xlsx", which contains demographic information, event-related potentials (ERPs), frequency band power, and microstate parameters for all participants.

Predicting Treatment Response to rTMS in Tinnitus Patients Using a Machine Learning Model Based on EEG and Clinical Features

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