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

doi:10.1007/978-3-540-72584-8_127

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 proceeding › Conference contribution

7 Scopus citations

Abstract

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 language	English (US)
Title of host publication	Computational Science - ICCS 2007 - 7th International Conference, Proceedings, Part I
Publisher	Springer Verlag
Pages	964-971
Number of pages	8
ISBN (Print)	9783540725831
DOIs	https://doi.org/10.1007/978-3-540-72584-8_127
State	Published - 2007
Externally published	Yes
Event	7th International Conference on Computational Science, ICCS 2007 - Beijing, China Duration: May 27 2007 → May 30 2007

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	4487 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	7th International Conference on Computational Science, ICCS 2007
Country/Territory	China
City	Beijing
Period	5/27/07 → 5/30/07

Keywords

Brain-machine interface
Forward-inverse models

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-540-72584-8_127

Cite this

DiGiovanna, J., Marchai, L., Rattanatamrong, P., Zhao, M., Darmanjian, S., Mahmoudi, B., Sanchez, J. C., Príncipe, J. C., Hermer-Vazquez, L., Figueiredo, R., & Fortes, J. A. B. (2007). Towards real-time distributed signal modeling for brain-machine interfaces. In Computational Science - ICCS 2007 - 7th International Conference, Proceedings, Part I (pp. 964-971). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4487 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-540-72584-8_127

Towards real-time distributed signal modeling for brain-machine interfaces. / DiGiovanna, Jack; Marchai, Loris; Rattanatamrong, Prapaporn et al.
Computational Science - ICCS 2007 - 7th International Conference, Proceedings, Part I. Springer Verlag, 2007. p. 964-971 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4487 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

DiGiovanna, J, Marchai, L, Rattanatamrong, P, Zhao, M, Darmanjian, S, Mahmoudi, B, Sanchez, JC, Príncipe, JC, Hermer-Vazquez, L, Figueiredo, R & Fortes, JAB 2007, Towards real-time distributed signal modeling for brain-machine interfaces. in Computational Science - ICCS 2007 - 7th International Conference, Proceedings, Part I. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4487 LNCS, Springer Verlag, pp. 964-971, 7th International Conference on Computational Science, ICCS 2007, Beijing, China, 5/27/07. https://doi.org/10.1007/978-3-540-72584-8_127

DiGiovanna J, Marchai L, Rattanatamrong P, Zhao M, Darmanjian S, Mahmoudi B et al. Towards real-time distributed signal modeling for brain-machine interfaces. In Computational Science - ICCS 2007 - 7th International Conference, Proceedings, Part I. Springer Verlag. 2007. p. 964-971. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-72584-8_127

DiGiovanna, Jack ; Marchai, Loris ; Rattanatamrong, Prapaporn et al. / Towards real-time distributed signal modeling for brain-machine interfaces. Computational Science - ICCS 2007 - 7th International Conference, Proceedings, Part I. Springer Verlag, 2007. pp. 964-971 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{ac057f3f374d4b8aa0c341f6ed69fa6c,

title = "Towards real-time distributed signal modeling for brain-machine interfaces",

abstract = "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.",

keywords = "Brain-machine interface, Forward-inverse models",

author = "Jack DiGiovanna and Loris Marchai and Prapaporn Rattanatamrong and Ming Zhao and Shalom Darmanjian and Babak Mahmoudi and Sanchez, {Justin C.} and Pr{\'i}ncipe, {Jos{\'e} C.} and Linda Hermer-Vazquez and Renato Figueiredo and Fortes, {Jos{\'e} A.B.}",

year = "2007",

doi = "10.1007/978-3-540-72584-8_127",

language = "English (US)",

isbn = "9783540725831",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "964--971",

booktitle = "Computational Science - ICCS 2007 - 7th International Conference, Proceedings, Part I",

note = "7th International Conference on Computational Science, ICCS 2007 ; Conference date: 27-05-2007 Through 30-05-2007",

}

TY - GEN

T1 - Towards real-time distributed signal modeling for brain-machine interfaces

AU - DiGiovanna, Jack

AU - Marchai, Loris

AU - Rattanatamrong, Prapaporn

AU - Zhao, Ming

AU - Darmanjian, Shalom

AU - Mahmoudi, Babak

AU - Sanchez, Justin C.

AU - Príncipe, José C.

AU - Hermer-Vazquez, Linda

AU - Figueiredo, Renato

AU - Fortes, José A.B.

PY - 2007

Y1 - 2007

N2 - 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.

AB - 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.

KW - Brain-machine interface

KW - Forward-inverse models

UR - http://www.scopus.com/inward/record.url?scp=37249069101&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=37249069101&partnerID=8YFLogxK

U2 - 10.1007/978-3-540-72584-8_127

DO - 10.1007/978-3-540-72584-8_127

M3 - Conference contribution

AN - SCOPUS:37249069101

SN - 9783540725831

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 964

EP - 971

BT - Computational Science - ICCS 2007 - 7th International Conference, Proceedings, Part I

PB - Springer Verlag

T2 - 7th International Conference on Computational Science, ICCS 2007

Y2 - 27 May 2007 through 30 May 2007

ER -

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

Abstract

Publication series

Other

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this