Development of a generative model of magnetoencephalography noise that enables brain signal extraction from single-epoch data

We presented a method of rejecting sensor-specific and environmental noise during magnetoencephalography (MEG) measurement that enables the extraction of brain signals from single-epoch data. The method assumes a parametric generative model of MEG data. The model’s optimal parameters were determined...

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Veröffentlicht in:	Medical & biological engineering & computing 2013-08, Vol.51 (8), p.937-951
Hauptverfasser:	Uno, Yutaka, Amano, Kaoru, Takeda, Tsunehiro
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Bayes Theorem Bayesian analysis Biological engineering Biomedical and Life Sciences Biomedical engineering Biomedical Engineering and Bioengineering Biomedicine Biosensors Brain Brain - physiology Brain research Computer Applications Decomposition Electroencephalography Extraction Human Physiology Humans Imaging Kalman filters Magnetoencephalography Magnetoencephalography - methods Methods Models, Theoretical Noise Noise reduction Optimization Original Article Physiology Radiology Sensors Signal processing Signal Processing, Computer-Assisted Software Spatial filtering Studies
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