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

Yidan Ding, Chalisa Udompanyawit, Yisha Zhang and Bin He ()
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Yidan Ding: Carnegie Mellon University
Chalisa Udompanyawit: Carnegie Mellon University
Yisha Zhang: Carnegie Mellon University
Bin He: Carnegie Mellon University

Nature Communications, 2025, vol. 16, issue 1, 1-20

Abstract: 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.

Date: 2025
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DOI: 10.1038/s41467-025-61064-x

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