Prediction of EMG from Multiple Electrode Recordings in Primary Motor Cortex

Research output: Contribution to journalConference article

3 Scopus citations

Abstract

An analysis of activity of primary motor cortex (M1) and muscle activity in the form of EMGs suggests that M1 contains a large amount of muscle related information. The simultaneous activity of a population of M1 neurons was recorded using a multi-electrode, intracortical array while recording arm muscle EMGs from a rhesus monkey during a multi-target, reaching task. Using multiple-input system identification techniques, linear, non-parametric filters relating the firing rates of the neuronal signals to the muscle EMGs were estimated. These filters were able to reconstruct and predict EMG signals directly from the recorded neuronal firing rates with a high level of accuracy. Initial cross-validations of the filters suggest that the relationships between M1 and muscles contain time and task dependencies as the quality of the predictions drop for longer time periods between filter estimation and EMG prediction and for cross-validations that involve movements to individual targets. Further tests involving more data sets and cross-validations are necessary to determine more precisely the temporal and task-specific stability of these filters. This will greatly increase our knowledge of how arm movement and muscle activity is encoded in the brain and offer practical applications in the harvesting of neural control signals.

Original languageEnglish (US)
Pages (from-to)2197-2200
Number of pages4
JournalAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume3
StatePublished - Dec 1 2003
EventA New Beginning for Human Health: Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Cancun, Mexico
Duration: Sep 17 2003Sep 21 2003

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Keywords

  • Arm movement
  • Multiple neurons
  • Muscle activity
  • Primate

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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