Non-invasive electromyographic speech neuroprosthesis: a geometric perspective

In this article, we present a high-bandwidth egocentric neuromuscular speech interface for translating silently voiced speech articulations into textand audio. Specifically, we collect electromyogram (EMG) signals from multiple articulatorysites on the face and neck as individuals articulate speech in an alaryngeal manner to perform EMG-to-text or EMG-to-audio translation. Such an interface is useful for restoring audible speech in individuals who have lost the ability to speak intelligibly due to laryngectomy, neuromuscular disease, stroke, or trauma-induced damage (e.g., radiotherapy toxicity) to speech articulators. Previous works have focused on training text or speech synthesis models using EMG collected during audible speech articulations or by transferring audio targets from EMG collected during audible articulation to EMG collected during silent articulation. However, such paradigms are not suited for individuals who have already lost the ability to audibly articulate speech. We are the first to present an alignment-free EMG-to-text and EMG-to-audio conversion using only EMG collected during silently articulated speech in an open-sourced manner. On a limited vocabulary corpora, our approach achieves almost 2.4x improvement in word error rate with a model that is 25x smaller by leveraging the inherent geometry of EMG.

Geometry of orofacial neuromuscular signals: speech articulation decoding using surface electromyography

Each year, millions of individuals lose the ability to speak intelligibly due to causes such as neuromuscular disease, stroke, trauma, and head/neck cancer surgery (e.g. laryngectomy) or treatment (e.g. radiotherapy toxicity to the speech articulators). Effective communication is crucial for daily activities, and losing the ability to speak leads to isolation, depression, anxiety, and a host of detrimental sequelae. Noninvasive surface electromyography (sEMG) has shown promise to restore speech output in these individuals. The goal is to collect sEMG signals from multiple articulatory sites as people silently produce speech and then decode the signals to enable fluent and natural communication. Currently, many fundamental properties of orofacial neuromuscular signals relating to speech articulation remain unanswered. They include questions relating to 1) the data structure of the orofacial sEMG signals, 2)the signal distribution shift of sEMG across individuals, 3) ability of sEMG signals to span the entire English language phonetic space during silent speech articulations, and 4) the generalization capability of non-invasive sEMG based silent speech interfaces. We address these questions through a series of experiments involving healthy human subjects. We show that sEMG signals evince graph data structure and that the signal distribution shift is given by a change of basis. Furthermore, we show that silently voiced articulations spanning the entire English language phonetic space can be decoded using small neural networks which can be trained with little data and that such architectures work well across individuals. To ensure transparency and reproducibility, we open-source all the data and codes used in this study.

Upper limb surface electromyography -- geometry, spectral characteristics, temporal evolution, and demographic confounds

Brain-body-computer interfaces aim to provide a fluid and natural way for humans to interact with technology. Among noninvasive interfaces, surface electromyogram (sEMG) signals have shown particular utility. However, much remains unknown about how sEMG is affected by various physiological and anatomical factors and how these confounds might affect gesture decoding across individuals or groups. In this article, we show that sEMG signals evince non-Euclidean graph data structure that is defined by a set of orthogonal axes and explain the signal distribution shift across individuals. We provide a dataset of upper limb sEMG signals and physiological measures of 91 adults as they perform 10 different hand gestures. Participants were selected to be representative of various age groups (18to 92 years) and BMI (healthy, overweight, and obese). Additional anatomical or physiological measures that might impact sEMG signals were also collected, such as skin hydration and elasticity. The article describes the inherent structure of sEMG data and provides methods to construct differentiable signal features that can be used with machine learning algorithms that use backpropagation. We then analyze how those parameters correlate with various physiological measures to probe if they can induce bias against (or towards) certain population groups. We find that higher frequencies in sEMG, although comprising less power than lower ones, provide better gesture decoding and show less bias with regard to demographic, circumstantial, and physiological confounds (such as age, skin hydration, and skin elasticity).

Topology of Surface Electromyogram Signals: Hand Gesture Decoding on Riemannian Manifolds

Decoding gestures from the upper limb using noninvasive surface electromyogram (sEMG) signals is of keen interest for the rehabilitation of amputees, artificial supernumerary limb augmentation, gestural control of computers, and virtual/augmented realities. We show that sEMG signals recorded across an array of sensor electrodes in multiple spatial locations around the forearm evince a rich geometric pattern of global motor unit (MU) activity that can be leveraged to distinguish different hand gestures. We demonstrate a simple technique to analyze spatial patterns of muscle MU activity within a temporal window and show that distinct gestures can be classified in both supervised and unsupervised manners. Specifically, we construct symmetric positive definite (SPD) covariance matrices to represent the spatial distribution of MU activity in a time window of interest, calculated as pairwise covariance of electrical signals measured across different electrodes. This allows us to understand and manipulate multivariate sEMG timeseries on a more natural subspace -the Riemannian manifold. Furthermore, it directly addresses signal variability across individuals and sessions, which remains a major challenge in the field. sEMG signals measured at a single electrode lack contextual information such as how various anatomical and physiological factors influence the signals and how their combined effect alters the evident interaction among neighboring muscles. As we show here, analyzing spatial patterns using covariance matrices on Riemannian manifolds allows us to robustly model complex interactions across spatially distributed MUs and provides a flexible and transparent framework to quantify differences in sEMG signals across individuals. The proposed method is novel in the study of sEMG signals and its performance exceeds the current benchmarks while maintaining exceptional computational efficiency.