Decoding high level signals for asynchronous brain machine interfaces

Byron Olson; Jennie Si; Jason Silver

doi:10.1109/IEMBS.2006.260876

Decoding high level signals for asynchronous brain machine interfaces

Byron Olson, Jennie Si, Jason Silver

Electrical Engineering

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

1 Scopus citations

Abstract

While many brain machine interface (BMI) systems have been presented in the literature, most of these systems present the user with an 'always on' interface with no way to shut the interface down when not needed. This paper proposes two extensions of previous BMI work to create an asynchronous BMI in which the system only produces outputs when needed. The first classifies incoming signals into not only task related states, but also an idle state. A refinement of this system utilizes a Markov Model (MM) of the task to impose order on the sequence of states produced by the system. This MM filter improves the accuracy of the system an average of 16%.

Original language	English (US)
Title of host publication	28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06
Pages	6569-6572
Number of pages	4
DOIs	https://doi.org/10.1109/IEMBS.2006.260876
State	Published - Dec 1 2006
Event	28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06 - New York, NY, United States Duration: Aug 30 2006 → Sep 3 2006

Publication series

Name	Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
ISSN (Print)	0589-1019

Other

Other	28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06
Country/Territory	United States
City	New York, NY
Period	8/30/06 → 9/3/06

ASJC Scopus subject areas

Signal Processing
Biomedical Engineering
Computer Vision and Pattern Recognition
Health Informatics

Access to Document

10.1109/IEMBS.2006.260876

Cite this

Olson, B., Si, J., & Silver, J. (2006). Decoding high level signals for asynchronous brain machine interfaces. In 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06 (pp. 6569-6572). Article 4030601 (Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings). https://doi.org/10.1109/IEMBS.2006.260876

Decoding high level signals for asynchronous brain machine interfaces. / Olson, Byron; Si, Jennie; Silver, Jason.
28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06. 2006. p. 6569-6572 4030601 (Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings).

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

Olson, B, Si, J & Silver, J 2006, Decoding high level signals for asynchronous brain machine interfaces. in 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06., 4030601, Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings, pp. 6569-6572, 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06, New York, NY, United States, 8/30/06. https://doi.org/10.1109/IEMBS.2006.260876

@inproceedings{05eac9c05a4e4c7a82991113994443d3,

title = "Decoding high level signals for asynchronous brain machine interfaces",

abstract = "While many brain machine interface (BMI) systems have been presented in the literature, most of these systems present the user with an 'always on' interface with no way to shut the interface down when not needed. This paper proposes two extensions of previous BMI work to create an asynchronous BMI in which the system only produces outputs when needed. The first classifies incoming signals into not only task related states, but also an idle state. A refinement of this system utilizes a Markov Model (MM) of the task to impose order on the sequence of states produced by the system. This MM filter improves the accuracy of the system an average of 16%.",

author = "Byron Olson and Jennie Si and Jason Silver",

year = "2006",

month = dec,

day = "1",

doi = "10.1109/IEMBS.2006.260876",

language = "English (US)",

isbn = "1424400325",

series = "Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings",

pages = "6569--6572",

booktitle = "28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06",

note = "28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06 ; Conference date: 30-08-2006 Through 03-09-2006",

}

TY - GEN

T1 - Decoding high level signals for asynchronous brain machine interfaces

AU - Olson, Byron

AU - Si, Jennie

AU - Silver, Jason

PY - 2006/12/1

Y1 - 2006/12/1

N2 - While many brain machine interface (BMI) systems have been presented in the literature, most of these systems present the user with an 'always on' interface with no way to shut the interface down when not needed. This paper proposes two extensions of previous BMI work to create an asynchronous BMI in which the system only produces outputs when needed. The first classifies incoming signals into not only task related states, but also an idle state. A refinement of this system utilizes a Markov Model (MM) of the task to impose order on the sequence of states produced by the system. This MM filter improves the accuracy of the system an average of 16%.

AB - While many brain machine interface (BMI) systems have been presented in the literature, most of these systems present the user with an 'always on' interface with no way to shut the interface down when not needed. This paper proposes two extensions of previous BMI work to create an asynchronous BMI in which the system only produces outputs when needed. The first classifies incoming signals into not only task related states, but also an idle state. A refinement of this system utilizes a Markov Model (MM) of the task to impose order on the sequence of states produced by the system. This MM filter improves the accuracy of the system an average of 16%.

UR - http://www.scopus.com/inward/record.url?scp=34047100383&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=34047100383&partnerID=8YFLogxK

U2 - 10.1109/IEMBS.2006.260876

DO - 10.1109/IEMBS.2006.260876

M3 - Conference contribution

AN - SCOPUS:34047100383

SN - 1424400325

SN - 9781424400324

T3 - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings

SP - 6569

EP - 6572

BT - 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06

T2 - 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06

Y2 - 30 August 2006 through 3 September 2006

ER -

Decoding high level signals for asynchronous brain machine interfaces

Abstract

Publication series

Other

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this