# Review of electroencephalography and electromyography research in robotics: opportunities and challenges

**Authors:** Zefeng Wang, Meiyan Xu, Junfeng Yao, Yue Yu, Bingbing Hu, Yufei Wang, Yu Wang, Xiaopeng Zhang

PMC · DOI: 10.1186/s42492-026-00216-2 · 2026-03-20

## TL;DR

This paper reviews how EEG and EMG are used in robotics to improve human-machine interfaces and explores the challenges and opportunities in this field.

## Contribution

The paper provides a comprehensive review of EEG and EMG integration in robotics, highlighting both current methods and future directions.

## Key findings

- EEG and EMG can function independently or together to control robotic systems.
- Real-time integration of these signals with robotics presents significant challenges.
- Innovative solutions are being developed to improve seamless human-machine interaction.

## Abstract

In the evolving nexus of neuroscience and robotics, the symbiotic fusion of electroencephalography (EEG) and electromyography (EMG) is emerging as a paradigm-shifting avenue for enhancing human-machine interfaces. While EEG, which captures the subtle electrical nuances of the brain, offers a potent channel for nuanced brain-machine communication, EMG serves as a bridge, converting neuromuscular intentions into actionable directives for robotic apparatuses. This review highlights the current methodologies in which EEG and EMG not only function in silos but also converge harmoniously to dictate robotic control. By delving deeper into this, the intricate synergy between cognitive processes, muscular responses, and machine actions can be unraveled. Subsequently, the discourse also navigates through the myriad challenges encountered in realizing real-time, seamless integration of these bio-signals with robotics and the innovative solutions poised to address them. The aim is to provide a comprehensive understanding of the interplay between neuroscience and robotics. This insight will help drive breakthroughs in adaptive human-machine collaboration.

## Full-text entities

- **Genes:** SEMG1 (semenogelin 1) [NCBI Gene 6406] {aka CT103, SEMG, SGI, dJ172H20.2}, EP300 (EP300 lysine acetyltransferase) [NCBI Gene 2033] {aka KAT3B, MKHK2, RSTS2, p300}
- **Diseases:** Parkinson's disease (MESH:D010300), cord injuries (MESH:D013119), muscle (MESH:D019042), neurological diseases (MESH:D020271), post (MESH:D000094025), trauma (MESH:D014947), infection (MESH:D007239), post-stroke (MESH:D020521), mobility decline (MESH:D014086), LSTM (MESH:D000088562), fatigue (MESH:D005221), brain damage (MESH:D001925), neurological disorders (MESH:D009461), nEMG (MESH:C000719195), allergic reactions (MESH:D004342), MI (MESH:D000068079), amyotrophic lateral sclerosis (MESH:D000690), limb loss (MESH:D001259), gait disorders (MESH:D020233), disabilities (MESH:D009069), neurological injury (MESH:D020196)
- **Chemicals:** carbon nanotube (MESH:D037742), polyamide (MESH:D009757), polyester (MESH:D011091), Pt (MESH:D010984), Au (MESH:D006046), Silver (MESH:D012834), MAV (-), nickel (MESH:D009532), polymer (MESH:D011108), polydimethylsiloxane (MESH:C013830), carbon (MESH:D002244), silver chloride (MESH:C037548), steel (MESH:D013232), saline (MESH:D012965), Sn (MESH:D014001), copper (MESH:D003300), polyurethane (MESH:D011140)
- **Species:** Homo sapiens (human, species) [taxon 9606]
- **Mutations:** Eto S, start/stop

## Figures

13 figures with captions in the complete paper: https://tomesphere.com/paper/PMC13003060/full.md

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Source: https://tomesphere.com/paper/PMC13003060