Simultaneous multi-area recordings in macaque frontoparietal cortex during a manual object-interaction task
- 1Lingang Laboratory
Abstract
Dexterous hand control is essential for daily activities and a top restoration priority for people with tetraplegia, yet neurophysiological datasets suitable for studying and decoding naturalistic hand behavior remain scarce. Most available recordings constrain movements to a small number of degrees of freedom or cover only a single stereotypical grasp-and-hold structure. Moreover, previous brain-computer interface (BCI) studies have largely relied on signals from a single cortical area, even though dexterous hand control is distributed across an interconnected frontoparietal network. To address these limitations, we designed a paradigm that elicits rich hand movements across 40 distinct hand-object interaction conditions. We recorded simultaneous frontoparietal activity with hand kinematics from a rhesus macaque performing them. Neural activity was recorded from the primary motor cortex, anterior intraparietal area, and ventral premotor cortex using microelectrode arrays spanning 1,920 channels, while a multi-camera markerless motion-capture system acquired video from which kinematics were extracted offline. The dataset comprises data from 20 sessions, including processed neural features, synchronized 3D coordinates of hand and elbow keypoints, and trial-level metadata including task conditions and event timing. We further provide technical validation, including a neural decoding benchmark with multiple algorithms for continuous keypoint coordination reconstruction. We found that cross-day and cross-task decoding performance varied among algorithms. Variation in channel availability leads to different performance degradation curves in different algorithms. This task-rich dataset offers a valuable resource for investigating the neural mechanisms of dexterous hand control and for advancing the performance of motor BCIs.
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BibTeX
@misc{liu2026simultaneous,
title = {{Simultaneous multi-area recordings in macaque frontoparietal cortex during a manual object-interaction task}},
author = {K. Liu and S. Guan and S. Luo and Y. Li and Y. Dong and W. Chen and M. Chen and D. Zhang and L. Chen and S. Huang and Y. Chen and H. Jiang and L. Wu and R. Li and F. Zhu and Y. Xie and Y. Sui and B. Min and S. Guan and C. Li and T. Wang and T. He},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.29.754877},
url = {https://www.biorxiv.org/content/10.64898/2026.09.29.754877v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Simultaneous multi-area recordings in macaque frontoparietal cortex during a manual object-interaction task
AU - K. Liu
AU - S. Guan
AU - S. Luo
AU - Y. Li
AU - Y. Dong
AU - W. Chen
AU - M. Chen
AU - D. Zhang
AU - L. Chen
AU - S. Huang
AU - Y. Chen
AU - H. Jiang
AU - L. Wu
AU - R. Li
AU - F. Zhu
AU - Y. Xie
AU - Y. Sui
AU - B. Min
AU - S. Guan
AU - C. Li
AU - T. Wang
AU - T. He
PY - 2026
DA - 2026/09/29
PB - bioRxiv
DO - 10.64898/2026.09.29.754877
UR - https://www.biorxiv.org/content/10.64898/2026.09.29.754877v1
AB - Dexterous hand control is essential for daily activities and a top restoration priority for people with tetraplegia, yet neurophysiological datasets suitable for studying and decoding naturalistic hand behavior remain scarce. Most available recordings constrain movements to a small number of degrees of freedom or cover only a single stereotypical grasp-and-hold structure. Moreover, previous brain-computer interface (BCI) studies have largely relied on signals from a single cortical area, even though dexterous hand control is distributed across an interconnected frontoparietal network. To address these limitations, we designed a paradigm that elicits rich hand movements across 40 distinct hand-object interaction conditions. We recorded simultaneous frontoparietal activity with hand kinematics from a rhesus macaque performing them. Neural activity was recorded from the primary motor cortex, anterior intraparietal area, and ventral premotor cortex using microelectrode arrays spanning 1,920 channels, while a multi-camera markerless motion-capture system acquired video from which kinematics were extracted offline. The dataset comprises data from 20 sessions, including processed neural features, synchronized 3D coordinates of hand and elbow keypoints, and trial-level metadata including task conditions and event timing. We further provide technical validation, including a neural decoding benchmark with multiple algorithms for continuous keypoint coordination reconstruction. We found that cross-day and cross-task decoding performance varied among algorithms. Variation in channel availability leads to different performance degradation curves in different algorithms. This task-rich dataset offers a valuable resource for investigating the neural mechanisms of dexterous hand control and for advancing the performance of motor BCIs.
ER -