IEEE J Biomed Health Inform. 2023 Jul;27(7):3187-3197. doi: 10.1109/JBHI.2023.3264521. Epub 2023 Jun 30.
Electroencephalogram (EEG) is an important technology to explore the central nervous mechanism of tinnitus. However, it is hard to obtain consistent results in many previous studies for the high heterogeneity of tinnitus. In order to identify tinnitus and provide theoretical guidance for the diagnosis and treatment, we propose a robust, data-efficient multi-task learning framework called Multi-band EEG Contrastive Representation Learning (MECRL). In this study, we collect resting-state EEG data from 187 tinnitus patients and 80 healthy subjects to generate a high-quality large-scale EEG dataset on tinnitus diagnosis, and then apply the MECRL framework on the generated dataset to obtain a deep neural network model which can distinguish tinnitus patients from the healthy controls accurately. Subject-independent tinnitus diagnosis experiments are conducted and the result shows that the proposed MECRL method is significantly superior to other state-of-the-art baselines and can be well generalized to unseen topics. Meanwhile, visual experiments on key parameters of the model indicate that the high-classification weight electrodes of tinnitus' EEG signals are mainly distributed in the frontal, parietal and temporal regions. In conclusion, this study facilitates our understanding of the relationship between electrophysiology and pathophysiology changes of tinnitus and provides a new deep learning method (MECRL) to identify the neuronal biomarkers in tinnitus.
脑电图（EEG）是探索耳鸣中枢神经机制的重要技术。然而，由于耳鸣的高度异质性，许多先前的研究难以获得一致的结果。为了识别耳鸣并为诊断和治疗提供理论指导，我们提出了一种称为多频带 EEG 对比表示学习（MECRL）的稳健、高效数据的多任务学习框架。在这项研究中，我们从 187 名耳鸣患者和 80 名健康受试者中收集静息态 EEG 数据，生成一个用于耳鸣诊断的高质量大规模 EEG 数据集，然后将 MECRL 框架应用于生成的数据集，以获得能够准确区分耳鸣患者和健康对照者的深度神经网络模型。进行了受试者间的耳鸣诊断实验，结果表明，所提出的 MECRL 方法明显优于其他最先进的基线方法，并且可以很好地推广到未见的主题。同时，对模型关键参数的可视化实验表明，耳鸣 EEG 信号的高分类权重电极主要分布在前额、顶叶和颞叶区域。总之，这项研究有助于我们理解耳鸣的电生理和病理生理学变化之间的关系，并提供了一种新的深度学习方法（MECRL）来识别耳鸣中的神经元生物标志物。
