FRL Handwriting: Handwriting Decoding from Surface Electromyography

UC San Diego 2026 Dataset Open access

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Authors: Patrick Kaifosh, Thomas R. Reardon, CTRL-labs at Reality Labs
DOI: 10.82901/nemar.nm000106.v2.0.0
Cited by: 0
References: 1

Abstract

This dataset comprises wrist-based surface electromyography (sEMG) recordings from 100 participants performing imagined handwriting tasks. Participants wrote prompted text in air without a physical writing surface, using a 16-channel sEMG wristband sampled at 2000 Hz. The dataset includes approximately 700 sessions (~7 per participant) and serves as a benchmark for developing non-invasive neuromotor interfaces for text entry in augmented/virtual reality and mobile computing applications.

Fields of Study & Subjects

Muscle activation and electromyography studies EEG and Brain-Computer Interfaces Gaze Tracking and Assistive Technology

Publication details

Journal / source
UC San Diego
Publication date
10 July 2026
Language
EN
Document type
Dataset