Classification of the myoelectric signal using time-frequency based representations
โ Scribed by K Englehart; B Hudgins; P.A Parker; M Stevenson
- Book ID
- 104376649
- Publisher
- Elsevier Science
- Year
- 1999
- Tongue
- English
- Weight
- 169 KB
- Volume
- 21
- Category
- Article
- ISSN
- 1350-4533
No coin nor oath required. For personal study only.
โฆ Synopsis
An accurate and computationally efficient means of classifying surface myoelectric signal patterns has been the subject of considerable research effort in recent years. Effective feature extraction is crucial to reliable classification and, in the quest to improve the accuracy of transient myoelectric signal pattern classification, an ensemble of time-frequency based representations are proposed. It is shown that feature sets based upon the short-time Fourier transform, the wavelet transform, and the wavelet packet transform provide an effective representation for classification, provided that they are subject to an appropriate form of dimensionality reduction.
๐ SIMILAR VOLUMES
AI~STRACT : A generalized category of cone-shaped kernels is proposed. Analysis of the kernel in the 2-D time, 2-D frequency, and ambiguity domains is pet;formed. The shape of this kernel in the 2-D time plane is bow-tie, which effectively suppresses cross-terms especially in the frequency direction
This paper deals with the time-frequency analysis of deterministic and stochastic non-stationary signals. It includes the following: a brief review of the fundamentals of time-frequency analysis and various time-frequency distributions; a summary of the inter-relations between time-frequency distrib