Concurrent Prediction of Finger Forces Based on Source Separation and Classification of Neuron Discharge Information

Yang Zheng, Xiaogang Hu

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

Abstract

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 (0.71 ± 0.11 versus 0.61 ± 0.09), a lower prediction error (5.88 ± 1.34% MVC versus 7.56 ± 1.60% MVC), and a higher accuracy in finger state (rest/active) prediction (88.10 ± 4.65% versus 80.21 ± 4.32%). 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.

Original languageEnglish (US)
Article number2150010
JournalInternational journal of neural systems
Volume31
Issue number6
DOIs
StatePublished - Jun 2021

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications

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