Parameter estimation for maximizing controllability of linear brain-machine interfaces

Brain-machine interfaces (BMIs) must be carefully designed for closed-loop control to ensure the best possible performance. The Kalman filter (KF) is a recursive linear BMI algorithm which has been shown to smooth cursor kinematics and improve control over non-recursive linear methods. However, recu...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society Ročník 2012; s. 1314 - 1317
Hlavní autori: Gowda, S., Orsborn, A. L., Carmena, J. M.
Médium: Konferenčný príspevok.. Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.01.2012
Predmet:
Algorithms
Animals
Biomechanical Phenomena - physiology
Brain-Computer Interfaces
Computer Simulation
Controllability
Correlation
Decoding
Electrodes, Implanted
Error analysis
Feedback control
Kinematics
Linear Models
Macaca mulatta
Male
Motor Cortex - physiology
Neurons - physiology
Signal Processing, Computer-Assisted
Task Performance and Analysis
Trajectory
ISBN:1424441196, 9781424441198
ISSN:1094-687X, 1557-170X, 2694-0604, 2694-0604
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Abstract Brain-machine interfaces (BMIs) must be carefully designed for closed-loop control to ensure the best possible performance. The Kalman filter (KF) is a recursive linear BMI algorithm which has been shown to smooth cursor kinematics and improve control over non-recursive linear methods. However, recursive estimators are not without their drawbacks. Here we show that recursive decoders can decrease BMI controllability by coupling kinematic variables that the subject might expect to be unrelated. For instance, a 2D neural cursor where velocity is controlled using a KF can increase the difficulty of straight reaches by linking horizontal and vertical velocity estimates. These effects resemble force fields in arm control. Analysis of experimental data from one non-human primate controlling a position/velocity KF cursor in closed-loop shows that the presence of these force-field effects correlated with decreased performance. We designed a modified KF parameter estimation algorithm to eliminate these effects. Cursor controllability improved significantly when our modifications were used in a closed-loop BMI simulator. Thus, designing highly controllable BMIs requires parameter estimation techniques that carefully craft relationships between decoded variables.
AbstractList Brain-machine interfaces (BMIs) must be carefully designed for closed-loop control to ensure the best possible performance. The Kalman filter (KF) is a recursive linear BMI algorithm which has been shown to smooth cursor kinematics and improve control over non-recursive linear methods. However, recursive estimators are not without their drawbacks. Here we show that recursive decoders can decrease BMI controllability by coupling kinematic variables that the subject might expect to be unrelated. For instance, a 2D neural cursor where velocity is controlled using a KF can increase the difficulty of straight reaches by linking horizontal and vertical velocity estimates. These effects resemble force fields in arm control. Analysis of experimental data from one non-human primate controlling a position/velocity KF cursor in closed-loop shows that the presence of these force-field effects correlated with decreased performance. We designed a modified KF parameter estimation algorithm to eliminate these effects. Cursor controllability improved significantly when our modifications were used in a closed-loop BMI simulator. Thus, designing highly controllable BMIs requires parameter estimation techniques that carefully craft relationships between decoded variables.
Brain-machine interfaces (BMIs) must be carefully designed for closed-loop control to ensure the best possible performance. The Kalman filter (KF) is a recursive linear BMI algorithm which has been shown to smooth cursor kinematics and improve control over non-recursive linear methods. However, recursive estimators are not without their drawbacks. Here we show that recursive decoders can decrease BMI controllability by coupling kinematic variables that the subject might expect to be unrelated. For instance, a 2D neural cursor where velocity is controlled using a KF can increase the difficulty of straight reaches by linking horizontal and vertical velocity estimates. These effects resemble force fields in arm control. Analysis of experimental data from one non-human primate controlling a position/velocity KF cursor in closed-loop shows that the presence of these force-field effects correlated with decreased performance. We designed a modified KF parameter estimation algorithm to eliminate these effects. Cursor controllability improved significantly when our modifications were used in a closed-loop BMI simulator. Thus, designing highly controllable BMIs requires parameter estimation techniques that carefully craft relationships between decoded variables.Brain-machine interfaces (BMIs) must be carefully designed for closed-loop control to ensure the best possible performance. The Kalman filter (KF) is a recursive linear BMI algorithm which has been shown to smooth cursor kinematics and improve control over non-recursive linear methods. However, recursive estimators are not without their drawbacks. Here we show that recursive decoders can decrease BMI controllability by coupling kinematic variables that the subject might expect to be unrelated. For instance, a 2D neural cursor where velocity is controlled using a KF can increase the difficulty of straight reaches by linking horizontal and vertical velocity estimates. These effects resemble force fields in arm control. Analysis of experimental data from one non-human primate controlling a position/velocity KF cursor in closed-loop shows that the presence of these force-field effects correlated with decreased performance. We designed a modified KF parameter estimation algorithm to eliminate these effects. Cursor controllability improved significantly when our modifications were used in a closed-loop BMI simulator. Thus, designing highly controllable BMIs requires parameter estimation techniques that carefully craft relationships between decoded variables.
Author Orsborn, A. L.
Gowda, S.
Carmena, J. M.
Author_xml – sequence: 1
  givenname: S.
  surname: Gowda
  fullname: Gowda, S.
  organization: Dept. of Electr. Eng. & Comput. Sci. (EECS), Univ. of California Berkeley, Berkeley, CA, USA
– sequence: 2
  givenname: A. L.
  surname: Orsborn
  fullname: Orsborn, A. L.
  organization: UCB-UCSF Grad. Group in Bioeng., Univ. of California Berkeley, Berkeley, CA, USA
– sequence: 3
  givenname: J. M.
  surname: Carmena
  fullname: Carmena, J. M.
  email: carmena@eecs.berkeley.edu
  organization: Dept. of EECS, Univ. of California Berkeley, Berkeley, CA, USA
BackLink https://www.ncbi.nlm.nih.gov/pubmed/23366140$$D View this record in MEDLINE/PubMed
BookMark eNo9kE1LAzEQhuMX2mp_gAiyRy9bM8lsPo5a_IKKHlS8lWSbaGQ3q9ktqL_egNW5DMM8vMwzY7Idu-gIOQQ6BaD69OL2fDZlFNhUcBQg9QaZaKkAKylBKgmbZMSExpIKiltkDMgQEUCLbTLKAVgKJZ_3yKTv32guBYpT3CV7jHMhAOmIPN2bZFo3uFS4fgitGUIXC9-lojWfoQ3fIb4UdReH1DWNsaEJw1fR-aIJ0ZlU2GRCLFtTv-a5CDHneFO7_oDseNP0brLu--Tx8uJhdl3O765uZmfzMnDEoeTK6MoaVcslGnCm4mKpvBVC17XnleXZBXVFrWKMe64QrcquttIavAfg--TkN_c9dR-rbLBoQ1-7fGp03apfAFNcMg2syujxGl3Z1i0X7ynbpq_F3y8ycPQLBOfc_3r9ef4DIRpx1A
ContentType Conference Proceeding
Journal Article
DBID 6IE
6IH
CBEJK
RIE
RIO
CGR
CUY
CVF
ECM
EIF
NPM
7X8
DOI 10.1109/EMBC.2012.6346179
DatabaseName IEEE Electronic Library (IEL) Conference Proceedings
IEEE Proceedings Order Plan (POP) 1998-present by volume
IEEE Xplore All Conference Proceedings
IEEE Electronic Library (IEL)
IEEE Proceedings Order Plans (POP) 1998-present
Medline
MEDLINE
MEDLINE (Ovid)
MEDLINE
MEDLINE
PubMed
MEDLINE - Academic
DatabaseTitle MEDLINE
Medline Complete
MEDLINE with Full Text
PubMed
MEDLINE (Ovid)
MEDLINE - Academic
DatabaseTitleList MEDLINE
MEDLINE - Academic
Database_xml – sequence: 1
  dbid: NPM
  name: PubMed
  url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed
  sourceTypes: Index Database
– sequence: 2
  dbid: RIE
  name: IEEE Electronic Library (IEL)
  url: https://ieeexplore.ieee.org/
  sourceTypes: Publisher
– sequence: 3
  dbid: 7X8
  name: MEDLINE - Academic
  url: https://search.proquest.com/medline
  sourceTypes: Aggregation Database
DeliveryMethod fulltext_linktorsrc
Discipline Engineering
EISBN 9781457717871
1457717875
EISSN 2694-0604
EndPage 1317
ExternalDocumentID 23366140
6346179
Genre orig-research
Journal Article
GroupedDBID 6IE
6IF
6IH
AAJGR
ACGFS
AFFNX
ALMA_UNASSIGNED_HOLDINGS
CBEJK
M43
RIE
RIO
RNS
29F
29G
6IK
6IM
CGR
CUY
CVF
ECM
EIF
IPLJI
NPM
7X8
ID FETCH-LOGICAL-i344t-38a95ba8c7d4a1ea536d8fb669ccf35b31194950b8223f3844b8060b5991ff113
IEDL.DBID RIE
ISBN 1424441196
9781424441198
ISICitedReferencesCount 4
ISICitedReferencesURI http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000313296501143&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN 1094-687X
1557-170X
2694-0604
IngestDate Thu Oct 02 19:39:08 EDT 2025
Thu Jan 02 22:16:24 EST 2025
Wed Aug 27 02:44:23 EDT 2025
IsDoiOpenAccess false
IsOpenAccess true
IsPeerReviewed true
IsScholarly true
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-i344t-38a95ba8c7d4a1ea536d8fb669ccf35b31194950b8223f3844b8060b5991ff113
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
OpenAccessLink https://zenodo.org/record/1267798
PMID 23366140
PQID 1283729125
PQPubID 23479
PageCount 4
ParticipantIDs proquest_miscellaneous_1283729125
ieee_primary_6346179
pubmed_primary_23366140
PublicationCentury 2000
PublicationDate 2012-01-01
PublicationDateYYYYMMDD 2012-01-01
PublicationDate_xml – month: 01
  year: 2012
  text: 2012-01-01
  day: 01
PublicationDecade 2010
PublicationPlace United States
PublicationPlace_xml – name: United States
PublicationTitle 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society
PublicationTitleAbbrev EMBC
PublicationTitleAlternate Conf Proc IEEE Eng Med Biol Soc
PublicationYear 2012
Publisher IEEE
Publisher_xml – name: IEEE
SSID ssj0000818304
ssj0020051
ssj0061641
ssib061542107
ssib053545923
ssib042469959
Score 1.9363327
Snippet Brain-machine interfaces (BMIs) must be carefully designed for closed-loop control to ensure the best possible performance. The Kalman filter (KF) is a...
SourceID proquest
pubmed
ieee
SourceType Aggregation Database
Index Database
Publisher
StartPage 1314
SubjectTerms Algorithms
Animals
Biomechanical Phenomena - physiology
Brain-Computer Interfaces
Computer Simulation
Controllability
Correlation
Decoding
Electrodes, Implanted
Error analysis
Feedback control
Kinematics
Linear Models
Macaca mulatta
Male
Motor Cortex - physiology
Neurons - physiology
Signal Processing, Computer-Assisted
Task Performance and Analysis
Trajectory
Title Parameter estimation for maximizing controllability of linear brain-machine interfaces
URI https://ieeexplore.ieee.org/document/6346179
https://www.ncbi.nlm.nih.gov/pubmed/23366140
https://www.proquest.com/docview/1283729125
Volume 2012
WOSCitedRecordID wos000313296501143&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LT8JAEJ4g8aAXH6Dig6yJRwsts-1urxKIByUclHAju-1uQiJgChj117vTFvSgB29tstumM5POt_P4BuAmUiZQAUXu3e_PIwTtSSNiL0Sh0likHZNn8EcPYjCQ43E8rMDtthfGGJMXn5kWXea5_HSRrClU1o6QO4cb78COEKLo1drGU4iaDeloUR62yNryTGfMvUiK8aapiwdBwedHXE_lvSzTnW5xu_d416WKr06rfBvRBSOSG_PLCSx_g9HcKfUP_vc5h1D_7u5jw63fOoKKmR_D_g9iwhqMhoqKtpzMGbFwFO2NzOFbNlPv09n0061iZZX7S8H0_cEWlhFmVRnTNHfCm-V1moYRI0VmqfSrDs_93lP33isnMHhT5HzloVRxqJVMRMpVYFSIUSqtjqI4SSyGGp3w3AnL1w5moEXJuZZ-5OvQoU5rgwBPoDpfzM0ZMK5I81aINFFcYKIx8a1EG1k_RaHDBtRIQpPXgmRjUgqnAdcbWU-c4VM2Q83NYr2cBMTb04kdQGvAaaGE7eaNws5_f-gF7JGGi0jKJVRX2dpcwW7ytpous6azrrFs5tb1BeBCxS8
linkProvider IEEE
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LT8MwDLbGQwIuvGE8g8SRQjOnTXoFgYYY0w6AdquSNpEmsQ2NDQG_nrjtBgc4cGulpFVtq_7ix2eA01hbrjlF7v3vLyAEHSgrkyBCqfNE5g1bZPCfWrLdVt1u0qnB2awXxlpbFJ_Zc7oscvn5MJtQqOwiRuEdbjIHC5EQDV52a80iKkTOhnS4qI5bZG9FrjMRQaxkd9rWJTgvGf2I7am6V1XC0y--uL6_vKKar8Z59T4iDEYkRxZWM1j-hqOFW7pZ_d8HrcHWd38f68w81zrU7GADVn5QE27CU0dT2ZaXOiMejrLBkXmEy_r6vdfvffpVrKpzfy65vj_Y0DFCrXrEDE2eCPpFpaZlxEkxclT8tQWPN9cPV82gmsEQ9FCIcYBKJ5HRKpO50NzqCONcORPHSZY5jAx64fkzVmg80ECHSgijwjg0kcedznGO2zA_GA7sLjChSfdOyjzTQmJmMAudQhe7MEdpojpskoTSl5JmI62EU4eTqaxTb_qUz9ADO5y8ppyYexqJh2h12CmVMNs8Vdje7w89hqXmw30rbd227_ZhmbRdxlUOYH48mthDWMzexr3X0VFhY1-rnceO
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=2012+Annual+International+Conference+of+the+IEEE+Engineering+in+Medicine+and+Biology+Society&rft.atitle=Parameter+estimation+for+maximizing+controllability+of+linear+brain-machine+interfaces&rft.au=Gowda%2C+S.&rft.au=Orsborn%2C+A.+L.&rft.au=Carmena%2C+J.+M.&rft.date=2012-01-01&rft.pub=IEEE&rft.isbn=9781424441198&rft.issn=1094-687X&rft.spage=1314&rft.epage=1317&rft_id=info:doi/10.1109%2FEMBC.2012.6346179&rft_id=info%3Apmid%2F23366140&rft.externalDocID=6346179
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1094-687X&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1094-687X&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1094-687X&client=summon