Objective: Robust neural decoding of intended motor output is crucial to enable intuitive control of assistive devices, such as robotic hands, to perform daily tasks. Few existing neural decoders can predict kinetic and kinematic variables simultaneously. The current study developed a continuous neural decoding approach that can concurrently predict fingertip forces and joint angles of multiple fingers. Methods: We obtained motoneuron firing activities by decomposing high-density electromyogram (HD EMG) signals of the extrinsic finger muscles. The identified motoneurons were first grouped and then refined specific to each finger (index or middle) and task (finger force and dynamic movement) combination. The refined motoneuron groups (separate matrix) were then applied directly to new EMG data in real-time involving both finger force and dynamic movement tasks produced by both fingers. EMG-amplitude-based prediction was also performed as a comparison. Results: We found that the newly developed decoding approach outperformed the EMG-amplitude method for both finger force and joint angle estimations with a lower prediction error (Force: 3.47±0.43 vs 6.64±0.69% MVC, Joint Angle: 5.40±0.50° vs 12.8±0.65°) and a higher correlation (Force: 0.75±0.02 vs 0.66±0.05, Joint Angle: 0.94±0.01 vs 0.5±0.05) between the estimated and recorded motor output. The performance was also consistent for both fingers. Conclusion: The developed neural decoding algorithm allowed us to accurately and concurrently predict finger forces and joint angles of multiple fingers in real-time. Significance: Our approach can enable intuitive interactions with assistive robotic hands, and allow the performance of dexterous hand skills involving both force control tasks and dynamic movement control tasks.
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Concurrent Prediction of Finger Forces Based on Source Separation and Classification of Neuron Discharge Information
A reliable neural-machine interface is essential for humans to intuitively interact with advanced robotic hands in an unconstrained environment. Existing neural decoding approaches utilize either discrete hand gesture-based pattern recognition or continuous force decoding with one finger at a time. We developed a neural decoding technique that allowed continuous and concurrent prediction of forces of different fingers based on spinal motoneuron firing information. High-density skin-surface electromyogram (HD-EMG) signals of finger extensor muscle were recorded, while human participants produced isometric flexion forces in a dexterous manner (i.e. produced varying forces using either a single finger or multiple fingers concurrently). Motoneuron firing information was extracted from the EMG signals using a blind source separation technique, and each identified neuron was further classified to be associated with a given finger. The forces of individual fingers were then predicted concurrently by utilizing the corresponding motoneuron pool firing frequency of individual fingers. Compared with conventional approaches, our technique led to better prediction performances, i.e. a higher correlation ([Formula: see text] versus [Formula: see text]), a lower prediction error ([Formula: see text]% MVC versus [Formula: see text]% MVC), and a higher accuracy in finger state (rest/active) prediction ([Formula: see text]% versus [Formula: see text]%). Our decoding method demonstrated the possibility of classifying motoneurons for different fingers, which significantly alleviated the cross-talk issue of EMG recordings from neighboring hand muscles, and allowed the decoding of finger forces individually and concurrently. The outcomes offered a robust neural-machine interface that could allow users to intuitively control robotic hands in a dexterous manner.
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- Award ID(s):
- 1847319
- NSF-PAR ID:
- 10220192
- Date Published:
- Journal Name:
- International Journal of Neural Systems
- ISSN:
- 0129-0657
- Page Range / eLocation ID:
- 2150010
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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A reliable and functional neural interface is necessary to control individual finger movements of assistive robotic hands. Non-invasive surface electromyogram (sEMG) can be used to predict fingertip forces and joint kinematics continuously. However, concurrent prediction of kinematic and dynamic variables in a continuous manner remains a challenge. The purpose of this study was to develop a neural decoding algorithm capable of concurrent prediction of fingertip forces and finger dynamic movements. High-density electromyogram (HD-EMG) signal was collected during finger flexion tasks using either the index or middle finger: isometric, dynamic, and combined tasks. Based on the data obtained from the two first tasks, motor unit (MU) firing activities associated with individual fingers and tasks were derived using a blind source separation method. MUs assigned to the same tasks and fingers were pooled together to form MU pools. Twenty MUs were then refined using EMG data of a combined trial. The refined MUs were applied to a testing dataset of the combined task, and were divided into five groups based on the similarity of firing patterns, and the populational discharge frequency was determined for each group. Using the summated firing frequencies obtained from five groups of MUs in a multivariate linear regression model, fingertip forces and joint angles were derived concurrently. The decoding performance was compared to the conventional EMG amplitude-based approach. In both joint angles and fingertip forces, MU-based approach outperformed the EMG amplitude approach with a smaller prediction error (Force: 5.36±0.47 vs 6.89±0.39 %MVC, Joint Angle: 5.0±0.27° vs 12.76±0.40°) and a higher correlation (Force: 0.87±0.05 vs 0.73±0.1, Joint Angle: 0.92±0.05 vs 0.45±0.05) between the predicted and recorded motor output. The outcomes provide a functional and accurate neural interface for continuous control of assistive robotic hands.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1142/S0129065721500106
