Abstract

Artifacts in magnetoneurography data due to endogenous biological noise sources, like the cardiac signal, can be four orders of magnitude higher than the signal of interest. Therefore, it is important to establish effective artifact reduction methods. We propose a blind source separation algorithm using only second-order temporal correlations for cleaning biomagnetic measurements of evoked responses in the peripheral nervous system. The algorithm showed its efficiency by eliminating disturbances originating from biological and technical noise sources and successfully extracting the signal of interest. This yields a significant improvement of the neuro-magnetic source analysis.

Keywords

Artifact (error)SIGNAL (programming language)Blind signal separationNoise reductionNoise (video)Reduction (mathematics)Computer scienceSignal-to-noise ratio (imaging)Artificial intelligenceSignal processingSource separationPattern recognition (psychology)AlgorithmSpeech recognitionMathematicsDigital signal processingTelecommunications

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Publication Info

Year
2000
Type
article
Volume
47
Issue
1
Pages
75-87
Citations
141
Access
Closed

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Andreas Ziehe, K. Müller, Guido Nolte et al. (2000). Artifact reduction in magnetoneurography based on time-delayed second-order correlations. IEEE Transactions on Biomedical Engineering , 47 (1) , 75-87. https://doi.org/10.1109/10.817622

Identifiers

DOI
10.1109/10.817622