Interpreting Neural Activity Through Linear and Nonlinear Models for Brain Machine Interfaces

Justin C. Sanchez, Deniz Erdogmus, Yadunandana Rao, Sung Phil Kim, Miguel Nicolelis, Johan Wessberg, Jose C. Principe

Research output: Contribution to journalConference article

9 Scopus citations

Abstract

Brain machine interface (BMI) design can be achieved by training linear and nonlinear models with simultaneously recorded cortical neural activity and behavior (typically the hand position of a primate). We propose the use of optimized BMI models for analyzing neural activity to assess the role of individual neurons and cortical areas in generating the performed movement. Two models (linear-feedforward and nonlinear-feedback) are trained to predict the hand position of a primate from neural recordings in a reaching task. Qualitative and quantitative investigation of the effect of neurons and their corresponding cortical areas through both models yields conclusions consistent with neurophysiologic knowledge. In addition, this analysis revealed the role of these areas and the importance of the neurons in terms of BMI design.

Original languageEnglish (US)
Pages (from-to)2160-2163
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

  • Analysis of neural activity
  • Brain machine interface
  • Linear/nonlinear models
  • Recurrent neural network

ASJC Scopus subject areas

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

Cite this

Sanchez, J. C., Erdogmus, D., Rao, Y., Kim, S. P., Nicolelis, M., Wessberg, J., & Principe, J. C. (2003). Interpreting Neural Activity Through Linear and Nonlinear Models for Brain Machine Interfaces. Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings, 3, 2160-2163.