Asynchronous decoding of dexterous finger movements using M1 neurons

Vikram Aggarwal, Soumyadipta Acharya, Francesco Tenore, Hyun Chool Shin, Ralph Etienne-Cummings, Marc H. Schieber, Nitish V. Thakor

Research output: Contribution to journalArticle

Abstract

Previous efforts in brain-machine interfaces (BMI) have looked at decoding movement intent or hand and arm trajectory, but current cortical control strategies have not focused on the decoding of 3 actions such as finger movements. The present work demonstrates the asynchronous decoding (i.e., where cues indicating the onset of movement are not known) of individual and combined finger movements. Single-unit activities were recorded sequentially from a population of neurons in the M1 hand area of trained rhesus monkeys during flexion and extension movements of each finger and the wrist. Nonlinear filters were designed to detect the onset of movement and decode the movement type from randomly selected neuronal ensembles (assembled from individually recorded single-unit activities). Average asynchronous decoding accuracies as high as 99.8%, 96.2%, and 90.5%, were achieved for individuated finger and wrist movements with three monkeys. Average decoding accuracy was still 92.5% when combined movements of two fingers were included. These results demonstrate that it is possible to asynchronously decode dexterous finger movements from a neuronal ensemble with high accuracy. This work takes an important step towards the development of a BMI for direct neural control of a state-of-the-art, multifingered hand prosthesis.

Original languageEnglish (US)
Pages (from-to)3-14
Number of pages12
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Volume16
Issue number1
DOIs
StatePublished - Feb 1 2008

Keywords

  • Brain-machine interface (BMI)
  • Dexterous control
  • Neural decoding
  • Neural interface
  • Neuroprosthesis

ASJC Scopus subject areas

  • Internal Medicine
  • Neuroscience(all)
  • Biomedical Engineering

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