ORIGINAL RESEARCH article
Front. Neurosci.
Sec. Neural Technology
Classifying Motion States from Neural Activity of Non-Human Primates for Brain-Computer Interfaces
YICONG XIAO 1
Spencer Kellis 2,3
Christopher Reiche 1,4
Florian Solzbacher 1,3,5,6
1. Department of Electrical and Computer Engineering, College of Engineering, University of Utah, Salt Lake City, United States, Utah, UT 84112
2. Neurorestoration Center, Keck School of Medicine, University of Southern California, Los Angeles, United States, California, 90033
3. Blackrock Neurotech, Salt Lake City, United States, Utah, 84108
4. Department of Engineering Sciences, Jade Hochschule, Wilhelmshaven, Germany
5. Department of Materials Science & Engineering, University of Utah, Salt Lake City, United States
6. Department of Biomedical Engineering, University of Utah, Salt Lake City, United States
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Abstract
Many brain-computer interface (BCI) systems record neural activity from the sensorimotor network to drive the velocity of a mouse cursor, robotic limb, or other similar end effectors. These systems accurately translate neural activity corresponding to imagined or attempted movement into control signals for movement of an effector. However, without explicit mechanisms to recognize the intended absence of movement, decoders continue to receive nonzero inputs from the neural activity and thus infer nonzero effector displacement, even though the intended behavior of the BCI user is to maintain a constant effector state. This paper proposes a scheme to classify intended effector stationary states versus movement states directly from neural activity. In offline analysis with intracortical premotor and primary motor recordings from two non-human primates, mean classification accuracy was 0.936 and 0.930, respectively, while preserving decoded trajectory continuity. These results suggest that the proposed scheme provides a reliable and accurate means of distinguishing between stationary and movement states, offering potential benefits for stable and safe BCI control.
Summary
Keywords
Brain-computer interface, Correlation analysis, motion states, neural activity, offline analysis, Principal Component Analysis, Support vector machine
Received
29 September 2025
Accepted
28 January 2026
Copyright
© 2026 XIAO, Kellis, Reiche and Solzbacher. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: YICONG XIAO
Disclaimer
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