用变分自编码器学习语音诱发脑电的主体不变表征

Lies Bollens, T. Francart, H. V. hamme
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引用次数: 9

摘要

脑电图(EEG)是了解大脑如何处理语言的有力方法。线性模型最近被深度神经网络所取代,并产生了令人鼓舞的结果。在相关的脑电分类领域中,明确建模主题不变性特征可以提高模型跨主题的泛化性,提高分类精度。在这项工作中,我们采用因式分层变分自编码器来利用相同刺激的并行脑电图记录。我们将EEG建模为两个分离的潜在空间。主题和内容潜空间的准确率分别达到98.96%和1.60%,而二元内容分类实验在主题和内容潜空间上的准确率分别达到51.51%和62.91%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Learning Subject-Invariant Representations from Speech-Evoked EEG Using Variational Autoencoders
The electroencephalogram (EEG) is a powerful method to understand how the brain processes speech. Linear models have recently been replaced for this purpose with deep neural networks and yield promising results. In related EEG classification fields, it is shown that explicitly modeling subject-invariant features improves generalization of models across subjects and benefits classification accuracy. In this work, we adapt factorized hierarchical variational autoencoders to exploit parallel EEG recordings of the same stimuli. We model EEG into two disentangled latent spaces. Subject accuracy reaches 98.96% and 1.60% on respectively the subject and content latent space, whereas binary content classification experiments reach an accuracy of 51.51% and 62.91% on respectively the subject and content latent space.
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