A cluster analysis of neuronal activity in the dorsal premotor cortical area for neuroprosthetic control.

N. Ye, A. Roontiva, J. He

Research output: Contribution to journalArticle

Abstract

With the use of the neuronal data acquisition technology, millisecond-level multi-electrode data from several regions of the premotor area were obtained from two rhesus monkeys trained to perform arm-reach tasks with visual cues in virtual reality. In each trial, animals were required to select and perform one of the four possible arm reaching movements to the target on the top-left or top-right of the virtual reality space. They were also required to decide whether they would move their arms straight to the target or curve them in order to avoid the obstacle that was presented. After the acquired neuronal signals were processed, unsupervised Hierarchical clustering and K-means clustering were performed to uncover the similarity and difference in the average firing rate of spike train data between neurons and phases for each experiment condition. The clustering results indicate the similarity of neuronal data in the movement planning and actual movement phases, and the difference of such data from the data in information processing phases. Furthermore, the clustering results show that when the target location is on the right, the move planning started earlier. The analysis of variance (ANOVA) on the neuronal data confirms the results from the hierarchical clustering.

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Motor Cortex
Cluster analysis
Virtual reality
Cluster Analysis
Planning
Analysis of variance (ANOVA)
Neurons
Data acquisition
Animals
Electrodes
Macaca mulatta
Automatic Data Processing
Experiments
Cues
Analysis of Variance
Technology

ASJC Scopus subject areas

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

Cite this

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title = "A cluster analysis of neuronal activity in the dorsal premotor cortical area for neuroprosthetic control.",
abstract = "With the use of the neuronal data acquisition technology, millisecond-level multi-electrode data from several regions of the premotor area were obtained from two rhesus monkeys trained to perform arm-reach tasks with visual cues in virtual reality. In each trial, animals were required to select and perform one of the four possible arm reaching movements to the target on the top-left or top-right of the virtual reality space. They were also required to decide whether they would move their arms straight to the target or curve them in order to avoid the obstacle that was presented. After the acquired neuronal signals were processed, unsupervised Hierarchical clustering and K-means clustering were performed to uncover the similarity and difference in the average firing rate of spike train data between neurons and phases for each experiment condition. The clustering results indicate the similarity of neuronal data in the movement planning and actual movement phases, and the difference of such data from the data in information processing phases. Furthermore, the clustering results show that when the target location is on the right, the move planning started earlier. The analysis of variance (ANOVA) on the neuronal data confirms the results from the hierarchical clustering.",
author = "N. Ye and A. Roontiva and J. He",
year = "2008",
language = "English (US)",
pages = "2638--2641",
journal = "Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference",
issn = "1557-170X",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

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AU - Ye, N.

AU - Roontiva, A.

AU - He, J.

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N2 - With the use of the neuronal data acquisition technology, millisecond-level multi-electrode data from several regions of the premotor area were obtained from two rhesus monkeys trained to perform arm-reach tasks with visual cues in virtual reality. In each trial, animals were required to select and perform one of the four possible arm reaching movements to the target on the top-left or top-right of the virtual reality space. They were also required to decide whether they would move their arms straight to the target or curve them in order to avoid the obstacle that was presented. After the acquired neuronal signals were processed, unsupervised Hierarchical clustering and K-means clustering were performed to uncover the similarity and difference in the average firing rate of spike train data between neurons and phases for each experiment condition. The clustering results indicate the similarity of neuronal data in the movement planning and actual movement phases, and the difference of such data from the data in information processing phases. Furthermore, the clustering results show that when the target location is on the right, the move planning started earlier. The analysis of variance (ANOVA) on the neuronal data confirms the results from the hierarchical clustering.

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