EEG2MOTION: Synthesizing Diverse Human Motions from EEG Signals during Motion Observation

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 diverse, 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 from motion-observation EEG, 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 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 anonymous project page: https://eeg2motion-demo-2026.github.io/EEG2MOTIONdemo_page/.