Spatial Feature Extraction for Classification of Nonstationary Myoelectric 
Signals

Hofmann, David; Herrmann, J. Michael

doi:10.1109/ICMLA.2012.222

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Spatial Feature Extraction for Classification of Nonstationary Myoelectric Signals

Hofmann, D., & Herrmann, J. M. (n.d.). Spatial Feature Extraction for Classification of Nonstationary Myoelectric Signals.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0029-1055-D Version Permalink: https://hdl.handle.net/11858/00-001M-0000-0029-1056-B

Genre: Conference Paper

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Creators:
Hofmann, David¹, Author
Herrmann, J. Michael¹, Author

Affiliations:
1Department of Nonlinear Dynamics, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society, ou_2063286

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Abstract: We compare classifiers for the classification of myoelectric signals and show that the performance can be improved by using spatial features that are extracted by independent component analysis. The obtained filters can be interpreted as reflecting the spatial structure of the data source. We find that the performance improves for several preprocessing algorithms, but it affects the relative performance for various classifiers in different ways. A critical performance difference is especially seen when non-stationary signal regimes during the onset of static contractions are included. Although a practically utilizable performance appears to be reached for the present data set by a certain combination of classification and preprocessing algorithms, it remains to be further optimized in order to keep this level for more realistic data sets.

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Language(s): eng - English

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Publication Status: Not specified

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Identifiers: eDoc: 634178
DOI: 10.1109/ICMLA.2012.222

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Title: 11th Conf. on Machine Learning and Application 2012

Place of Event: Boca Raton, Florida

Start-/End Date: 2012-12-12 - 2012-12-15

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