EEG2MOTION: Towards Open-Vocabulary Human Motion Synthesis from Non-invasive Brain Signals

[2608.14754] EEG2MOTION: Towards Open-Vocabulary Human Motion Synthesis from Non-invasive Brain Signals Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Electrical Engineering and Systems Science > Signal Processing arXiv:2608.14754 (eess) [Submitted on 14 Aug 2026] Title: EEG2MOTION: Towards Open-Vocabulary Human Motion Synthesis from Non-invasive Brain Signals Authors: Yulong Peng , Yijian Pan , Yuqi Yang , Nenggan Zheng , Weidong Chen , Xiaoling Hu , Shaomin Zhang View a PDF of the paper titled EEG2MOTION: Towards Open-Vocabulary Human Motion Synthesis from Non-invasive Brain Signals, by Yulong Peng and 6 other authors View PDF HTML (experimental) Abstract: Human motion is governed by a hierarchical motor system where the brain provides high-level intentions and lower-level structures coordinate detailed dynamics. Existing brain-computer interfaces (BCIs) typically oversimplify this into constrained classification or low-dimensional control, failing to capture the richness of natural movement. Bridging this gap to achieve open-vocabulary, full-body motion synthesis remains challenging due to the substantial cross-modal divergence between sparse neural signals and high-dimensional kinematics, as well as the lack of large-scale paired EEG-motion datasets. To address this, we introduce EEG2MOTION, the first EEG-motion-text dataset for human motion synthesis, comprising nearly 20,000 paired samples across thousands of motions. Using this dataset, we first demonstrate via multimodal contrastive learning that non-invasive EEG embeddings can be effectively aligned with text, video, and motion representations to decode high-level semantics. We then propose EEG-conditioned Masked Motion Model (EMMM), a generative framework that unites an EEG encoder with a motion decoder to synthesize continuous, full-body human motions directly from brain activity. Experimental results show that EMMM generates coherent and realistic motion sequences from non-invasive brain signals. To the best of our knowledge, this is the first work to generate diverse full-body human motions from non-invasive brain signals, opening a new direction toward generative and open-vocabulary motor BCIs. See our project page: this https URL . Subjects: Signal Processing (eess.SP) Cite as: arXiv:2608.14754 [eess.SP] (or arXiv:2608.14754v1 [eess.SP] for this version) https://doi.org/10.48550/arXiv.2608.14754 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Yulong Peng [ view email ] [v1] Fri, 14 Aug 2026 04:40:01 UTC (1,781 KB) Full-text links: Access Paper: View a PDF of the paper titled EEG2MOTION: Towards Open-Vocabulary Human Motion Synthesis from Non-invasive Brain Signals, by Yulong Peng and 6 other authors View PDF HTML (experimental) TeX Source view license Current browse context: eess.SP < prev | next > new | recent | 2026-08 Change to browse by: eess References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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