论文标题
与私人编码器向脑电图的域名表示
Domain-Invariant Representation Learning from EEG with Private Encoders
论文作者
论文摘要
基于深度学习的脑电图（EEG）信号处理方法因数据分布的变化而受到测试时间泛化的差。当隐私的代表性学习感兴趣时，例如在临床环境中，这将成为一个更具挑战性的问题。为此，我们提出了一个多源学习体系结构，从数据集特定的私人编码器中提取域不变表示。我们的模型利用了基于最大的均值拨款（MMD）域对准方法来实现编码表示的域 - 差异，这在基于EEG的情感分类中优于最先进的方法。此外，在我们的管道中学到的表示形式将域的隐私保密为特定于数据集的私人编码减轻了具有共同参数的常规，集中式脑电图的深度神经网络培训方法的需求。
Deep learning based electroencephalography (EEG) signal processing methods are known to suffer from poor test-time generalization due to the changes in data distribution. This becomes a more challenging problem when privacy-preserving representation learning is of interest such as in clinical settings. To that end, we propose a multi-source learning architecture where we extract domain-invariant representations from dataset-specific private encoders. Our model utilizes a maximum-mean-discrepancy (MMD) based domain alignment approach to impose domain-invariance for encoded representations, which outperforms state-of-the-art approaches in EEG-based emotion classification. Furthermore, representations learned in our pipeline preserve domain privacy as dataset-specific private encoding alleviates the need for conventional, centralized EEG-based deep neural network training approaches with shared parameters.
