IEEE J Biomed Health Inform. 2021 Aug;25(8):2928-2937. doi: 10.1109/JBHI.2021.3062335. Epub 2021 Aug 5.
Human computer interaction (HCI) involves a multidisciplinary fusion of technologies, through which the control of external devices could be achieved by monitoring physiological status of users. However, physiological biosignals often vary across users and recording sessions due to unstable physical/mental conditions and task-irrelevant activities. To deal with this challenge, we propose a method of adversarial feature encoding with the concept of a Rateless Autoencoder (RAE), in order to exploit disentangled, nuisance-robust, and universal representations. We achieve a good trade-off between user-specific and task-relevant features by making use of the stochastic disentanglement of the latent representations by adopting additional adversarial networks. The proposed model is applicable to a wider range of unknown users and tasks as well as different classifiers. Results on cross-subject transfer evaluations show the advantages of the proposed framework, with up to an 11.6% improvement in the average subject-transfer classification accuracy.
人机交互（HCI）涉及多学科技术的融合，通过监测用户的生理状态，可以实现对外部设备的控制。然而，由于不稳定的生理/心理状态和与任务无关的活动，生理生物信号在不同用户和记录会话中经常会发生变化。为了应对这一挑战，我们提出了一种基于无码自动编码器（RAE）概念的对抗特征编码方法，以利用解缠、抗干扰和通用的表示。我们通过采用额外的对抗网络来实现对潜在表示的随机解缠，从而在用户特定和任务相关特征之间取得良好的平衡。所提出的模型适用于更广泛的未知用户和任务以及不同的分类器。跨受试者转移评估的结果表明了该框架的优势，平均受试者转移分类精度提高了 11.6%。
