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A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy

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Objective Recognition of Tinnitus Location Using Electroencephalography Connectivity Features.

Zhaobo Li¹, Xinzui Wang^1,2, Weidong Shen³

¹Jihua Laboratory, Foshan, China.

Frontiers in Neuroscience

|January 21, 2022

Summary

Electroencephalography (EEG) connectivity features can diagnose tinnitus location. Pearson correlation coefficient (PCC) and phase-locking value (PLV) show high accuracy, enabling objective tinnitus diagnosis.

Keywords:

connectivity features deep learning algorithms objective recognition resting-state EEG tinnitus location

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Area of Science:

Neuroscience
Biomedical Engineering
Auditory Science

Background:

Tinnitus is a prevalent yet poorly understood auditory disorder.
Objective diagnostic methods for tinnitus are lacking, hindering timely clinical intervention.

Purpose of the Study:

To investigate electroencephalography (EEG) connectivity features as potential biomarkers for diagnosing chronic tinnitus.
To assess the efficacy of machine learning algorithms in classifying tinnitus based on EEG signal features.

Main Methods:

Resting-state EEG signals were recorded from tinnitus patients.
Four connectivity features (PLV, PLI, PCC, TE) and two time-frequency features were extracted.
Support vector machine (SVM), multi-layer perception (MLP), and convolutional neural network (CNN) algorithms were employed for classification.

Main Results:

High classification accuracies (99.42% for SVM with PCC, 99.1% for MLP with PCC) were achieved.
Phase-locking value (PLV) also demonstrated excellent classification performance.
The MLP algorithm provided the fastest computation time (4.2s), suitable for real-time diagnosis.

Conclusions:

EEG connectivity features effectively differentiate tinnitus locations.
PCC and PLV are promising biomarkers for objective tinnitus diagnosis.
These findings support clinicians in the initial diagnosis of tinnitus.