Use of the discriminant Fourier-derived cepstrum with feature-level post-processing for surface electromyographic signal classification.

Abstract:

:Myoelectrical pattern classification is a crucial part in multi-functional prosthesis control. This paper investigates a discriminant Fourier-derived cepstrum (DFC) and feature-level post-processing (FLPP) to discriminate hand and wrist motions using the surface electromyographic signal. The Fourier-derived cepstrum takes advantage of the Fourier magnitude or sub-band power energy of signals directly and provides flexible use of spectral information changing with different motions. Appropriate cepstral coefficients are selected by a proposed separability criterion to construct DFC features. For the post-processing, FLPP which combines features from several analysis windows is used to improve the feature performance further. In this work, two classifiers (a linear discriminant classifier and quadratic discriminant classifier) without hyper-parameter optimization are employed to simplify the training procedure and avoid the possible bias of feature evaluation. Experimental results of the 11-motion problem show that the proposed DFC feature outperforms traditional features such as time-domain statistics and autoregressive-derived cepstrum in terms of the classification accuracy, and it is a promising method for the multi-functionality and high-accuracy control of myoelectric prostheses.

journal_name

Physiol Meas

authors

Chen X,Zhu X,Zhang D

doi

10.1088/0967-3334/30/12/008

subject

Has Abstract

pub_date

2009-12-01 00:00:00

pages

1399-413

issue

12

eissn

0967-3334

issn

1361-6579

pii

S0967-3334(09)24402-9

journal_volume

30

pub_type

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