口面部神经肌肉信号的几何结构：基于表面肌电图的言语发音解码
Geometry of orofacial neuromuscular signals: speech articulation decoding using surface electromyography
AI总结：
本研究聚焦表面肌电信号的言语发音解码，发现对称正定矩阵流形是肌电信号的天然嵌入空间，通过线性变换实现代数解释并量化个体间分布偏移，为开发高效的言语神经假体神经网络提供了新方向。
摘要：
目的：本文提出了利用表面肌电（EMG）信号解码言语发音的数据与方法。基于EMG的言语神经假体为因喉切除术、神经肌肉疾病、中风或发音器官创伤性损伤（如放疗导致的损伤）而丧失清晰言语能力的人群恢复可听见的言语提供了一种有前景的途径。方法：为实现这一目标，我们在受试者发音时采集其面部、下颌和颈部的EMG信号，并开展EMG到语音的转换研究。主要结果：研究发现，对称正定（SPD）矩阵流形是EMG信号的天然嵌入空间。具体而言，我们利用线性变换对流形值EMG数据进行了代数解释，并分析和量化了不同个体间EMG信号的分布偏移。意义：总体而言，该方法在开发兼具数据效率和参数效率的神经网络方面展现出巨大潜力，这对于基于EMG的系统而言是重要考量因素——此类系统面临大规模数据采集的挑战，且需在嵌入式设备有限的计算资源下运行。
英文摘要：
Objective. In this article, we present data and methods for decoding speech articulations using surface electromyogram (EMG) signals. EMG-based speech neuroprostheses offer a promising approach for restoring audible speech in individuals who have lost the ability to speak intelligibly due to laryngectomy, neuromuscular diseases, stroke, or trauma-induced damage (e.g., from radiotherapy) to the speech articulators. Approach. To achieve this, we collect EMG signals from the face, jaw, and neck as subjects articulate speech, and we perform EMG-to-speech translation. Main results. Our findings reveal that the manifold of symmetric positive definite (SPD) matrices serves as a natural embedding space for EMG signals. Specifically, we provide an algebraic interpretation of the manifold-valued EMG data using linear transformations, and we analyze and quantify distribution shifts in EMG signals across individuals. Significance. Overall, our approach demonstrates significant potential for developing neural networks that are both data- and parameter-efficient, an important consideration for EMG-based systems, which face challenges in large-scale data collection and operate under limited computational resources on embedded devices.
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