A new architecture for deriving dynamic brain-machine interfaces

José Fortes, Renato Figueiredo, Linda Hermer-Vazquez, José Príncipe, Justin C. Sanchez

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

5 Citations (Scopus)

Abstract

Great potential exists for future Brain Machine Interfaces (BMIs) to help paralyzed patients, and others with motor disabilities, regain (artificial) motor control and autonomy. This paper describes a novel approach towards the development of new design architectures and research test-beds for advanced BMls. It addresses a critical design challenge in deriving the functional mapping between the subject's movement intent and actuated behavior. Currently, adaptive signal processing techniques are used to correlate neuronal modulation with known movements generated by the subject. However, with patients who are paralyzed, access to the individual's movement is unavailable. Inspired by motor control research, this paper considers a predictive framework for BMI using multiple adaptive models trained with supervised or reinforcement learning in a closed-loop architecture that requires real-time feedback. Here, movement trajectories can be inferred and incrementally updated using instantaneous knowledge of the movement target and the individual's current neuronal activation. In this framework, BMIs require a computing infrastructure capable of selectively executing multiple models on the basis of signals received by and/or provided to the brain in real time. Middleware currently under investigation to provide this data-driven dynamic capability is discussed.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages546-553
Number of pages8
Volume3993 LNCS - III
DOIs
StatePublished - Aug 7 2006
Externally publishedYes
EventICCS 2006: 6th International Conference on Computational Science - Reading, United Kingdom
Duration: May 28 2006May 31 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3993 LNCS - III
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

OtherICCS 2006: 6th International Conference on Computational Science
CountryUnited Kingdom
CityReading
Period5/28/065/31/06

Fingerprint

Brain-Computer Interfaces
Brain
Motor Control
Adaptive Signal Processing
Regain
Supervised learning
Reinforcement learning
Research Design
Middleware
Learning
Disability
Multiple Models
Supervised Learning
Signal processing
Reinforcement Learning
Data-driven
Testbed
Chemical activation
Correlate
Trajectories

ASJC Scopus subject areas

  • Computer Science(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Theoretical Computer Science

Cite this

Fortes, J., Figueiredo, R., Hermer-Vazquez, L., Príncipe, J., & Sanchez, J. C. (2006). A new architecture for deriving dynamic brain-machine interfaces. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3993 LNCS - III, pp. 546-553). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3993 LNCS - III). https://doi.org/10.1007/11758532_72

A new architecture for deriving dynamic brain-machine interfaces. / Fortes, José; Figueiredo, Renato; Hermer-Vazquez, Linda; Príncipe, José; Sanchez, Justin C.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3993 LNCS - III 2006. p. 546-553 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3993 LNCS - III).

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

Fortes, J, Figueiredo, R, Hermer-Vazquez, L, Príncipe, J & Sanchez, JC 2006, A new architecture for deriving dynamic brain-machine interfaces. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 3993 LNCS - III, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3993 LNCS - III, pp. 546-553, ICCS 2006: 6th International Conference on Computational Science, Reading, United Kingdom, 5/28/06. https://doi.org/10.1007/11758532_72
Fortes J, Figueiredo R, Hermer-Vazquez L, Príncipe J, Sanchez JC. A new architecture for deriving dynamic brain-machine interfaces. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3993 LNCS - III. 2006. p. 546-553. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/11758532_72
Fortes, José ; Figueiredo, Renato ; Hermer-Vazquez, Linda ; Príncipe, José ; Sanchez, Justin C. / A new architecture for deriving dynamic brain-machine interfaces. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3993 LNCS - III 2006. pp. 546-553 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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