Towards real-time distributed signal modeling for brain-machine interfaces

Jack DiGiovanna, Loris Marchai, Prapaporn Rattanatamrong, Ming Zhao, Shalom Darmanjian, Babak Mahmoudi, Justin C. Sanchez, José C. Príncipe, Linda Hermer-Vazquez, Renato Figueiredo, José A.B. Fortes

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

7 Scopus citations


New architectures for Brain-Machine Interface communication and control use mixture models for expanding rehabilitation capabilities of disabled patients. Here we present and test a dynamic data-driven (BMI) Brain-Machine Interface architecture that relies on multiple pairs of forward-inverse models to predict, control, and learn the trajectories of a robotic arm in a real-time closed-loop system. A method of window-RLS was used to compute the forward-inverse model pairs in real-time and a model switching mechanism based on reinforcement learning was used to test the ability to map neural activity to elementary behaviors. The architectures were tested with in vivo data and implemented using remote computing resources.

Original languageEnglish (US)
Title of host publicationComputational Science - ICCS 2007 - 7th International Conference, Proceedings, Part I
PublisherSpringer Verlag
Number of pages8
ISBN (Print)9783540725831
StatePublished - 2007
Externally publishedYes
Event7th International Conference on Computational Science, ICCS 2007 - Beijing, China
Duration: May 27 2007May 30 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4487 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other7th International Conference on Computational Science, ICCS 2007


  • Brain-machine interface
  • Forward-inverse models

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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