Recent developments in biosignal processing have enabled users to exploit their physiological status for manipulating devices in a reliable and safe manner. One major challenge of physiological sensing lies in the variability of biosignals across different users and tasks. To address this issue, we propose an adversarial feature extractor for transfer learning to exploit disentangled universal representations. We consider the trade-off between task-relevant features and user-discriminative information by introducing additional adversary and nuisance networks in order to manipulate the latent representations such that the learned feature extractor is applicable to unknown users and various tasks. Results on cross-subject transfer evaluations exhibit the benefits of the proposed framework, with up to $8.8\%$ improvement in average accuracy of classification, and demonstrate adaptability to a broader range of subjects.

中文翻译：

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用于主体不变生理特征提取的解缠结对抗自动编码器


生物信号处理的最新发展使用户能够利用他们的生理状态以可靠和安全的方式操纵设备。生理传感的一大挑战在于不同用户和任务之间生物信号的可变性。为了解决这个问题，我们提出了一种用于转移学习的对抗性特征提取器，以利用解开的通用表示。我们通过引入额外的对手和滋扰网络来考虑任务相关特征和用户区分信息之间的权衡，以便操纵潜在表示，使得学习到的特征提取器适用于未知用户和各种任务。跨学科转移评估的结果展示了所提出的框架的好处，分类的平均准确度提高了 8.8\%$，并证明了对更广泛学科的适应性。