Machine learning techniques have achieved great success in electromyography (EMG) decoding, but EMG-based cross-subject silent speech recognition (SSR) received less attention because of its high individual variability. Therefore, this article explores the field of cross-subject SSR to improve the recognition performance of EMG data collected from new subjects. First, this article reports on applying time-series features and 1-D convolutional neural networks (1D-CNNs) for cross-subject SSR. Second, this article proposes using a conditional domain adversarial network (CDAN) to solve the problem of reduced cross-subject SSR accuracy in the few samples’ data sets. It innovatively integrates the maximum mean difference (MMD) loss to get an improved CDAN (ICDAN). While 1D-CNN is a feature extraction network that can meet the needs of cross-subject SSR in large data sets, the recognition effect will be weakened in small data sets. Adding an ICDAN network after the feature extraction network can improve the problem of data distribution differences between the two domains, and further enhance recognition performance. The results show that the 1D-CNN model based on time-series features yields better results in the SSR of new subjects, and the ICDAN model can further improve the classification accuracy of cross-subjects in a few sample data sets by 14.88%.

中文翻译：

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使用条件域对抗网络的基于肌电图的跨主体无声语音识别


机器学习技术在肌电图（EMG）解码方面取得了巨大成功，但基于肌电图的跨主体无声语音识别（SSR）由于其较高的个体差异而受到的关注较少。因此，本文探索跨主题SSR领域，以提高从新主题收集的肌电数据的识别性能。首先，本文报告了如何将时间序列特征和一维卷积神经网络（1D-CNN）应用于跨主题 SSR。其次，本文提出使用条件域对抗网络（CDAN）来解决少数样本数据集中跨主题 SSR 准确性降低的问题。它创新性地集成了最大平均差（MMD）损失以获得改进的CDAN（ICDAN）。而1D-CNN是一种特征提取网络，可以满足大数据集跨主题SSR的需求，但在小数据集下识别效果会减弱。在特征提取网络之后添加ICDAN网络可以改善两个域之间数据分布差异的问题，进一步增强识别性能。结果表明，基于时间序列特征的1D-CNN模型在新受试者的SSR方面取得了较好的效果，而ICDAN模型可以进一步将少量样本数据集中的跨受试者分类准确率提高14.88%。