{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/splashnet-split-and-share-encoders-for","title":"SplashNet: Split-and-Share Encoders for Accurate and Efficient Typing with Surface Electromyography","arxiv_id":"2506.12356","date":"2025-06-14","proceeding":null,"authors":["Nima Hadidi","Jason Chan","Ebrahim Feghhi","Jonathan Kao"],"abstract":"Surface electromyography (sEMG) at the wrists could enable natural, keyboard-free text entry, yet the state-of-the-art emg2qwerty baseline still misrecognizes $51.8\\%$ of characters in the zero-shot setting on unseen users and $7.0\\%$ after user-specific fine-tuning. We trace many of these errors to mismatched cross-user signal statistics, fragile reliance on high-order feature dependencies, and the absence of architectural inductive biases aligned with the bilateral nature of typing. To address these issues, we introduce three simple modifications: (i) Rolling Time Normalization, which adaptively aligns input distributions across users; (ii) Aggressive Channel Masking, which encourages reliance on low-order feature combinations more likely to generalize across users; and (iii) a Split-and-Share encoder that processes each hand independently with weight-shared streams to reflect the bilateral symmetry of the neuromuscular system. Combined with a five-fold reduction in spectral resolution ($33\\!\\rightarrow\\!6$ frequency bands), these components yield a compact Split-and-Share model, SplashNet-mini, which uses only $\\tfrac14$ the parameters and $0.6\\times$ the FLOPs of the baseline while reducing character-error rate (CER) to $36.4\\%$ zero-shot and $5.9\\%$ after fine-tuning. An upscaled variant, SplashNet ($\\tfrac12$ the parameters, $1.15\\times$ the FLOPs of the baseline), further lowers error to $35.7\\%$ and $5.5\\%$, representing relative improvements of $31\\%$ and $21\\%$ in the zero-shot and fine-tuned settings, respectively. SplashNet therefore establishes a new state of the art without requiring additional data.","url_abs":"https://arxiv.org/abs/2506.12356v1","url_pdf":"https://arxiv.org/pdf/2506.12356v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"splashnet-split-and-share-encoders-for","repo_url":"https://github.com/nhadidi/splashnet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}