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...

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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
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Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 23
ISBN:1424441196
9781424441198
ISSN:1094-687X
1557-170X
2694-0604
2694-0604
DOI:10.1109/EMBC.2012.6346179