A real-time, high-performance brain-computer interface for finger decoding and quadcopter control

2024 · doi:10.1101/2024.02.06.578107
preprint OA: closed
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A novel brain-computer interface enabled a participant to control 3 finger groups and a virtual quadcopter, demonstrating high-performance decoding of multiple degrees-of-freedom for effector control.

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This paper developed a real-time, high-performance brain-computer interface (BCI) that decodes finger activity into continuous control of three independent finger groups using 2D thumb movements, and then repurposed the decoded finger positions for 4-DOF velocity control of a virtual quadcopter in obstacle courses. In a human research participant performing sequential reach-and-hold trials, the system achieved an average acquisition rate of 76 targets/min and completion time of 1.58 ± 0.06 seconds, performing favorably despite a two-fold increase in decoded degrees of freedom. A major limitation stated by the presentation is that the results are demonstrated in a single participant over sequential trials rather than a broader study population. This paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

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Abstract

People with paralysis express unmet needs for peer support, leisure activities, and sporting activities. Many within the general population rely on social media and massively multiplayer video games to address these needs. We developed a high-performance finger brain-computer-interface system allowing continuous control of 3 independent finger groups with 2D thumb movements. The system was tested in a human research participant over sequential trials requiring fingers to reach and hold on targets, with an average acquisition rate of 76 targets/minute and completion time of 1.58 ± 0.06 seconds. Performance compared favorably to previous animal studies, despite a 2-fold increase in the decoded degrees-of-freedom (DOF). Finger positions were then used for 4-DOF velocity control of a virtual quadcopter, demonstrating functionality over both fixed and random obstacle courses. This approach shows promise for controlling multiple-DOF end-effectors, such as robotic fingers or digital interfaces for work, entertainment, and socialization.
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Abstract People with paralysis express unmet needs for peer support, leisure activities, and sporting activities. Many within the general population rely on social media and massively multiplayer video games to address these needs. We developed a high-performance finger brain-computer-interface system allowing continuous control of 3 independent finger groups with 2D thumb movements. The system was tested in a human research participant over sequential trials requiring fingers to reach and hold on targets, with an average acquisition rate of 76 targets/minute and completion time of 1.58 ± 0.06 seconds. Performance compared favorably to previous animal studies, despite a 2-fold increase in the decoded degrees-of-freedom (DOF). Finger positions were then used for 4-DOF velocity control of a virtual quadcopter, demonstrating functionality over both fixed and random obstacle courses. This approach shows promise for controlling multiple-DOF end-effectors, such as robotic fingers or digital interfaces for work, entertainment, and socialization. Competing Interest Statement L.R.H.: Massachusetts General Hospital is a subcontractor for a NIH SBIR with Paradromics. The MGH Translational Research Center has clinical research support agreements with Neuralink, Synchron, Reach Neuro, Axoft, and Precision Neuro, for which LRH provides consultative input. J.M.H.: Consultant for Neuralink Corp, Enspire DBS, and Paradromics; equity (stock options) in MapLight Therapeutics. He is also an inventor of intellectual property licensed by Stanford University to Blackrock Neurotech and Neuralink Corp. The other authors declare no competing interests. Footnotes ↵† The work for this study was primarily completed at Stanford University. Competing interests: L.R.H.: Massachusetts General Hospital is a subcontractor for a NIH SBIR with Paradromics. The MGH Translational Research Center has clinical research support agreements with Neuralink, Synchron, Reach Neuro, Axoft, and Precision Neuro, for which LRH provides consultative input. J.M.H.: Consultant for Neuralink, Enspire DBS, and Paradromics; equity (stock options) in MapLight Therapeutics. He is also an inventor of intellectual property licensed by Stanford University to Blackrock Neurotech and Neuralink. The other authors declare no competing interests.

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