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EEG-based brain-computer interface enables real-time robotic hand control at individual finger level

Author

Listed:
  • Yidan Ding

    (Carnegie Mellon University)

  • Chalisa Udompanyawit

    (Carnegie Mellon University)

  • Yisha Zhang

    (Carnegie Mellon University)

  • Bin He

    (Carnegie Mellon University
    Carnegie Mellon University
    Carnegie Mellon University)

Abstract

Brain-computer interfaces (BCIs) connect human thoughts to external devices, offering the potential to enhance life quality for individuals with motor impairments and general population. Noninvasive BCIs are accessible to a wide audience but currently face challenges, including unintuitive mappings and imprecise control. In this study, we present a real-time noninvasive robotic control system using movement execution (ME) and motor imagery (MI) of individual finger movements to drive robotic finger motions. The proposed system advances state-of-the-art electroencephalography (EEG)-BCI technology by decoding brain signals for intended finger movements into corresponding robotic motions. In a study involving 21 able-bodied experienced BCI users, we achieved real-time decoding accuracies of 80.56% for two-finger MI tasks and 60.61% for three-finger tasks. Brain signal decoding was facilitated using a deep neural network, with fine-tuning enhancing BCI performance. Our findings demonstrate the feasibility of naturalistic noninvasive robotic hand control at the individuated finger level.

Suggested Citation

  • Yidan Ding & Chalisa Udompanyawit & Yisha Zhang & Bin He, 2025. "EEG-based brain-computer interface enables real-time robotic hand control at individual finger level," Nature Communications, Nature, vol. 16(1), pages 1-20, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-61064-x
    DOI: 10.1038/s41467-025-61064-x
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    References listed on IDEAS

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