omg2letters: Translating Muscle Activity into Written Language

Muhammad Salman Kabir, Gurgen Soghoyan, Mikhail A. Lebedev

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Keywords: Optomyography, Machine learning, Augmentation, Motor decoding, Electromyography, Human-machine interaction, Brain computer interfaces

Abstract: Despite significant advancements in brain-computer interface (BCI) technology, systems capable of leveraging physiological signals to detect and recognize human intentions in real-time are still underdeveloped. To achieve a new level of human-machine interaction, it is essential to integrate motor activity correlates with state-of-the-art artificial intelligence (AI) architectures. In this study, we present the first demonstration of handwriting decoding—a complex motor task—using a novel myographic method called Optomyography (OMG). Unlike previous electromyography (EMG)-based approaches that treat handwriting decoding as a classification problem, we frame it as a continuous trajectory reconstruction challenge. We evaluated GRUScribe (GRU-based decoder) and TransScribe (transformer-based decoder), successfully decoding 10 numerical digits and 33 Russian letters from 20 able-bodied and 4 amputee participants, without requiring elaborate preprocessing. Our results demonstrate the remarkable potential of OMG for recognizing complex motor activity. We believe that our work sets a new benchmark in non-invasive muscle activity decoding, offering direct applications in advanced prosthetic control and human-machine interfaces.

Track: 7. General Track

Registration Id: GKN33NP4YGJ

Submission Number: 83