arXiv subjects

Harshavardhana T. Gowda

Publications and source records attributed to Harshavardhana T. Gowda.

5 recordsLinked to original sources

arXiv2026

Non-invasive electromyographic speech neuroprosthesis: a geometric perspective

We present a neuromuscular speech interface that translates silently voiced articulations directly into text. We record surface electromyographic (EMG) signals from multiple articulatory sites on the face and neck as participants silently articulate speech, enabling direct EMG-to-text translation. Such an interface has the potential to restore communication for individuals who have lost the ability to produce intelligible speech due to laryngectomy, neuromuscular disease, stroke, or trauma-induced damage (e.g., radiotherapy toxicity) to the speech articulators. Prior work has largely focused on mapping EMG collected during audible articulation to time-aligned audio targets or transferring these targets to silent EMG recordings, which inherently requires audio and limits applicability to patients who can no longer speak. In contrast, we propose an efficient representation of high-dimensional EMG signals and demonstrate direct sequence-to-sequence EMG-to-text conversion at the phonemic level without relying on time-aligned audio.

eess.AS↗

arXiv2026

emg2speech: Synthesizing speech from electromyography using self-supervised speech models

We present a neuromuscular speech interface that translates electromyographic (EMG) signals recorded from orofacial muscles during speech articulation directly into audio. We find that self-supervised speech (S3) representations are strongly linearly related to the electrical power of muscle activity: a simple linear mapping predicts EMG power from S3 representations with a correlation of r = 0.85. In addition, EMG power vectors associated with distinct articulatory gestures form structured, separable clusters. Together, these observations suggest that S3 models implicitly encode articulatory mechanisms, as reflected in EMG activity. Leveraging this structure, we map EMG signals into the S3 representation space and synthesize speech, enabling end-to-end EMG-to-speech generation without explicit articulatory modeling or vocoder training. We demonstrate this system with a participant with amyotrophic lateral sclerosis (ALS), converting orofacial EMG recorded while she silently articulated speech into audio.

cs.SD↗

arXiv2025

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

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.

cs.CL↗

arXiv2025

A database of upper limb surface electromyogram signals from demographically diverse individuals

Upper limb based neuromuscular interfaces aim to provide a seamless way for humans to interact with technology. Among noninvasive interfaces, surface electromyogram (EMG) signals hold significant promise. However, their sensitivity to physiological and anatomical factors remains poorly understood, raising questions about how these factors influence gesture decoding across individuals or groups. To facilitate the study of signal distribution shifts across individuals or groups of individuals, we present a dataset of upper limb EMG signals and physiological measures from 91 demographically diverse adults. Participants were selected to represent a range of ages (18 to 92 years) and body mass indices (healthy, overweight, and obese). The dataset also includes measures such as skin hydration and elasticity, which may affect EMG signals. This dataset provides a basis to study demographic confounds in EMG signals and serves as a benchmark to test the development of fair and unbiased algorithms that enable accurate hand gesture decoding across demographically diverse subjects. Additionally, we validate the quality of the collected data using state-of-the-art gesture decoding techniques.

eess.SP↗

arXiv2025

Topology of surface electromyogram signals: hand gesture decoding on Riemannian manifolds

$\textit{Objective.}$ In this article, we present data and methods for decoding hand gestures using surface electromyogram (EMG) signals. EMG-based upper limb interfaces are valuable for amputee rehabilitation, artificial supernumerary limb augmentation, gestural control of computers, and virtual and augmented reality applications. $\textit{Approach.}$ To achieve this, we collect EMG signals from the upper limb using surface electrodes placed at key muscle sites involved in hand movements. Additionally, we design and evaluate efficient models for decoding EMG signals. $\textit{Main results.}$ Our findings reveal that the manifold of symmetric positive definite (SPD) matrices serves as an effective embedding space for EMG signals. Moreover, for the first time, we quantify the distribution shift of these signals across individuals. $\textit{Significance.}$ Overall, our approach demonstrates significant potential for developing efficient and interpretable methods for decoding EMG signals. This is particularly important as we move toward the broader adoption of EMG-based wrist interfaces.

eess.SP↗