Artifact reduction in magnetoneurography based on time-delayed second-order correlations

• Published in: IEEE transactions on biomedical engineering Vol. 47; no. 1; pp. 75 - 87
• Main Authors: Ziehe, A., Muller, K.-R., Nolte, G., Mackert, B.-M., Curio, G.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01-01-2000; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Biological and medical sciences; Biological information theory; Biomagnetics; Biomedical imaging; Biomedical measurements; Blind source separation; Correlation methods; Diagnostic Techniques, Neurological; Electric Stimulation; Electrophysiology; Evoked Potentials, Somatosensory; Humans; Independent component analysis; Investigative techniques, diagnostic techniques (general aspects); Lumbar Vertebrae; Magnetic analysis; Magnetic separation; Magnetism; Magnetoencephalography; Medical sciences; Nervous system; Neurophysiology; Pathology. Cytology. Biochemistry. Spectrometry. Miscellaneous investigative techniques; Radiculopathy - diagnosis; Signal processing algorithms; Signal Processing, Computer-Assisted; Tibial Nerve - physiology; Time Factors; Human; Peripheral nervous system; Second order; Magnetometry; Evoked magnetic field; Algorithm performance; Time correlation; Signal processing; Neurography; Nervous system; Artefact; Reliability
• ISSN: 0018-9294; 1558-2531
• DOI: 10.1109/10.817622

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.