Learning the contributions of the motor, premotor, and posterior parietal cortices for hand trajectory reconstruction in a brain machine interface

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

20 Scopus citations

Abstract

The ability to record, in real-time, the activity of hundreds of cortical neurons gives the ability to selectively study the function of clusters of cortical neurons in Brain Machine Interface (BMI) experiments. We have demonstrated using a recursive multilayer perceptron (RMLP) that using the appropriate signal processing theory in a well-chosen parsimonious model, we can develop constructs that agree with basic physiological modeling of neural control. By looking through the trained model, we have found interesting relationships between the neuronal firing and the movement. The RMLP allows us to continuously study the relationship between neural activity and behavior without the active interference of the experimenter. The findings presented in this study offer an opportunity for the neuroscience community to compare the cortical interactions as constructed by the RMLP to what is known about motor neurophysiology.

Original languageEnglish (US)
Title of host publicationConference Proceedings - 1st International IEEE EMBS Conference on Neural Engineering
EditorsLaura J. Wolf, Jodi L. Strock
PublisherIEEE Computer Society
Pages59-62
Number of pages4
ISBN (Electronic)0780375793
DOIs
StatePublished - 2003
Externally publishedYes
Event1st International IEEE EMBS Conference on Neural Engineering - Capri Island, Italy
Duration: Mar 20 2003Mar 22 2003

Publication series

NameInternational IEEE/EMBS Conference on Neural Engineering, NER
Volume2003-January
ISSN (Print)1948-3546
ISSN (Electronic)1948-3554

Other

Other1st International IEEE EMBS Conference on Neural Engineering
Country/TerritoryItaly
CityCapri Island
Period3/20/033/22/03

Keywords

  • Animals
  • Biomedical signal processing
  • Finite impulse response filter
  • Interference
  • Machine learning
  • Multilayer perceptrons
  • Neurons
  • Neurophysiology
  • Neuroscience
  • Predictive models

ASJC Scopus subject areas

  • Artificial Intelligence
  • Mechanical Engineering

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