Bayesian filtering of myoelectric signals

被引:100
作者
Sanger, Terence D. [1 ]
机构
[1] Stanford Univ, Med Ctr, Div Child Neurol & Movement Disorders, Stanford, CA 94305 USA
关键词
D O I
10.1152/jn.00936.2006
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
Surface electromyography is used in research, to estimate the activity of muscle, in prosthetic design, to provide a control signal, and in biofeedback, to provide subjects with a visual or auditory indication of muscle contraction. Unfortunately, successful applications are limited by the variability in the signal and the consequent poor quality of estimates. I propose to use a nonlinear recursive filter based on Bayesian estimation. The desired filtered signal is modeled as a combined diffusion and jump process and the measured electromyographic (EMG) signal is modeled as a random process with a density in the exponential family and rate given by the desired signal. The rate is estimated on-line by calculating the full conditional density given all past measurements from a single electrode. The Bayesian estimate gives the filtered signal that best describes the observed EMG signal. This estimate yields results with very low short-time variability but also with the capability of very rapid response to change. The estimate approximates isometric joint torque with lower error and higher signal-to-noise ratio than current linear methods. Use of the nonlinear filter significantly reduces noise compared with current algorithms, and it may therefore permit more effective use of the EMG signal for prosthetic control, biofeedback, and neurophysiology research.
引用
收藏
页码:1839 / 1845
页数:7
相关论文
共 46 条
[1]   Surface myoelectric signal classification for prostheses control [J].
Al-Assaf, Y. ;
Al-Nashash, H. .
Journal of Medical Engineering and Technology, 2005, 29 (05) :203-207
[2]   RECURSIVE NON-LINEAR ESTIMATION OF A DIFFUSION ACTING AS THE RATE OF AN OBSERVED POISSON-PROCESS [J].
BOEL, RK ;
BENES, VE .
IEEE TRANSACTIONS ON INFORMATION THEORY, 1980, 26 (05) :561-575
[3]   Recursive Bayesian decoding of motor cortical signals by particle filtering [J].
Brockwell, AE ;
Rojas, AL ;
Kass, RE .
JOURNAL OF NEUROPHYSIOLOGY, 2004, 91 (04) :1899-1907
[4]   An analysis of neural receptive field plasticity by point process adaptive filtering [J].
Brown, EN ;
Nguyen, DP ;
Frank, LM ;
Wilson, MA ;
Solo, V .
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2001, 98 (21) :12261-12266
[5]   Nonlinear filter design using Fokker-Planck-Kolmogorov probability density evolutions [J].
Challa, S ;
Bar-Shalom, Y .
IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS, 2000, 36 (01) :309-315
[6]   Continuous myoelectric control for powered prostheses using hidden Markov models [J].
Chan, ADC ;
Englehart, KB .
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 2005, 52 (01) :121-124
[7]   Hidden Markov model classification of myoelectric signals in speech [J].
Chan, ADC ;
Englehart, K ;
Hudgins, B ;
Lovely, DF .
IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE, 2002, 21 (05) :143-146
[8]   SINGLE-SITE ELECTROMYOGRAPH AMPLITUDE ESTIMATION [J].
CLANCY, EA ;
HOGAN, N .
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 1994, 41 (02) :159-167
[9]   Probability density of the surface electromyogram and its relation to amplitude detectors [J].
Clancy, EA ;
Hogan, N .
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 1999, 46 (06) :730-739
[10]   Relating agonist-antagonist electromyograms to joint torque during isometric, quasi-isotonic, nonfatiguing contractions [J].
Clancy, EA ;
Hogan, N .
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 1997, 44 (10) :1024-1028