Electroencephalogram (EEG)-based neurofeedback has been widely studied for tinnitus therapy in recent years. Most existing research relies on experts' cognitive prediction, and studies based on machine learning and deep learning are either data-hungry or not well generalizable to new subjects. In this paper, we propose a robust, data-efficient model for distinguishing tinnitus from the healthy state based on EEG-based tinnitus neurofeedback. We propose trend descriptor, a feature extractor with lower fineness, to reduce the effect of electrode noises on EEG signals, and a siamese encoder-decoder network boosted in a supervised manner to learn accurate alignment and to acquire high-quality transferable mappings across subjects and EEG signal channels. Our experiments show the proposed method significantly outperforms state-of-the-art algorithms when analyzing subjects' EEG neurofeedback to 90dB and 100dB sound, achieving an accuracy of 91.67%-94.44% in predicting tinnitus and control subjects in a subject-independent setting. Our ablation studies on mixed subjects and parameters show the method's stability in performance.

中文翻译：

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用于主观耳鸣听众耳鸣诊断的可推广样本高效连体自动编码器


近年来，基于脑电图（EEG）的神经反馈已被广泛研究用于耳鸣治疗。大多数现有研究依赖于专家的认知预测，而基于机器学习和深度学习的研究要么需要大量数据，要么不能很好地推广到新学科。在本文中，我们提出了一种基于脑电图的耳鸣神经反馈的稳健、数据高效的模型，用于区分耳鸣与健康状态。我们提出了趋势描述符，一种具有较低精细度的特征提取器，以减少电极噪声对脑电图信号的影响，以及以监督方式增强的连体编码器-解码器网络，以学习准确的对齐并获取跨受试者的高质量可转移映射脑电图信号通道。我们的实验表明，在分析受试者对 90dB 和 100dB 声音的 EEG 神经反馈时，所提出的方法显着优于最先进的算法，在独立于受试者的环境中预测耳鸣和控制受试者时达到 91.67%-94.44% 的准确度。我们对混合受试者和参数的消融研究表明了该方法的性能稳定性。