Decoding individuated finger movements using volume-constrained neuronal ensembles in the M1 hand area

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

Research output: Contribution to journalArticle

Abstract

Individuated finger and wrist movements can be decoded using random subpopulations of neurons that are widely distributed in the primary motor (M1) hand area. This work investigates 1) whether it is possible to decode dexterous finger movements using spatially-constrained volumes of neurons as typically recorded from a microelectrode array; and 2) whether decoding accuracy differs due to the configuration or location of the array within the M1 hand area. Single-unit activities were sequentially recorded from task-related neurons in two rhesus monkeys as they performed individuated movements of the fingers and the wrist. Simultaneous neuronal ensembles were simulated by constraining these activities to the recording field dimensions of conventional microelectrode array architectures. Artificial neural network (ANN) based filters were able to decode individuated finger movements with greater than 90% accuracy for the majority of movement types, using as few as 20 neurons from these ensemble activities. Furthermore, for the large majority of cases there were no significant differences (p

Original languageEnglish (US)
Pages (from-to)15-23
Number of pages9
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Volume16
Issue number1
DOIs
StatePublished - Feb 2008

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Keywords

  • Brain-machine interface
  • Microelectrode array
  • Neural networks
  • Neural prosthetics

ASJC Scopus subject areas

  • Rehabilitation
  • Biophysics
  • Bioengineering
  • Health Professions(all)

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