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Updated: Dec 20, 2025

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Published on: April 21, 2023

Decoding hand kinematics from population responses in sensorimotor cortex during grasping.

Elizaveta V Okorokova¹, James M Goodman, Nicholas G Hatsopoulos

¹Committee on Computational Neuroscience, University of Chicago, Chicago, IL, United States of America. Center for Bioelectric Interfaces, National Research University Higher School of Economics, Moscow, Russia.

Journal of Neural Engineering

|May 23, 2020

Summary

Researchers decoded dexterous hand movements using neural signals from the brain. This finding advances brain-machine interfaces for precise hand control.

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Last Updated: Dec 20, 2025

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Area of Science:

Neuroscience
Motor Control
Brain-Machine Interfaces

Background:

The hand's complex movements are crucial for object interaction.
Neural signals from the primary motor cortex (M1) and somatosensory cortex (SC) controlling the hand are less understood than those for the proximal upper limb.

Purpose of the Study:

To investigate neural representations of dexterous hand movements in M1 and SC.
To decode hand kinematics from M1 and SC activity.

Main Methods:

Two monkeys were trained to grasp objects of varying size and shape.
Hand postures were tracked, and single-unit activity was recorded from M1 and SC.
Population activity was used to decode hand kinematics across multiple joints.

Main Results:

Accurate decoding of hand kinematics was achieved using a limited number of neural signals.
Rostral M1 provided better decoding performance than caudal M1.
Brodmann's area 3a in SC outperformed areas 1 and 2.
Decoding performance was higher for joint angles than for joint angular velocities.

Conclusions:

Cortical signals are viable for dexterous hand control in brain-machine interface applications.
Somatosensory cortex (SC) postural representations can be leveraged for sensorimotor loop closure via intracortical stimulation.