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  1. Medical and Biological Engineering and Computing
  2. Medical and Biological Engineering and Computing : Volume 43
  3. Medical and Biological Engineering and Computing : Volume 43, Issue 5, October 2005
  4. Reduction of noise from magnetoencephalography data
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Medical and Biological Engineering and Computing : Volume 55
Medical and Biological Engineering and Computing : Volume 54
Medical and Biological Engineering and Computing : Volume 53
Medical and Biological Engineering and Computing : Volume 52
Medical and Biological Engineering and Computing : Volume 51
Medical and Biological Engineering and Computing : Volume 50
Medical and Biological Engineering and Computing : Volume 49
Medical and Biological Engineering and Computing : Volume 48
Medical and Biological Engineering and Computing : Volume 47
Medical and Biological Engineering and Computing : Volume 46
Medical and Biological Engineering and Computing : Volume 45
Medical and Biological Engineering and Computing : Volume 44
Medical and Biological Engineering and Computing : Volume 43
Medical and Biological Engineering and Computing : Volume 43, Issue 6, November 2005
Medical and Biological Engineering and Computing : Volume 43, Issue 5, October 2005
Evaluation of antidecubitus mattresses
Evaluation of a fall detector based on accelerometers: A pilot study
Instrumented staircase for kinetic analyses of upper-and lower-limb function during stair gait
Estimation of atrial fibrillatory wave from single-lead atrial fibrillation electrocardiograms using principal component analysis concepts
Atrial cell action potential parameter fitting using genetic algorithms
Suppression of the cardiac electric field artifact from the heart action evoked potential
Myocardial perfusion monitoring during coronary artery bypass using an electrocardiogram-triggered laser Doppler technique
Robust and self-tuning blood flow control during extracorporeal circulation in the presence of system parameter uncertainties
Amplitude and phase relationship between alpha and beta oscillations in the human electroencephalogram
Simulation of intra-orbital optic nerve electrical stimulation
Noise from implantable Cooper cable
Lidocaine transport through living rat skin using alternating current
Reduction of noise from magnetoencephalography data
Adaptive filtering of evoked potentials using higher-order adaptive signal enhancer with genetic-type variable step-size prefilter
Genetic algorithm for the efficient selection of disyllabic word lists used in Mandarin speech discrimination tests
In vitro measurements of optical properties of porcine brain using a novel compact device
CT—3D rotational angiography automatic registration: A sensitivity analysis
Automatic analysis of immunocytochemically stained tissue samples
Parametric image of myocardial blood flow generated from dynamic H$_{2}$ $^{15}$O PET using factor analysis and cluster analysis
Design and validation of an analyser to measure sulphur hexafluoride gas during respiration
Medical and Biological Engineering and Computing : Volume 43, Issue 4, August 2005
Medical and Biological Engineering and Computing : Volume 43, Issue 3, June 2005
Medical and Biological Engineering and Computing : Volume 43, Issue 2, April 2005
Medical and Biological Engineering and Computing : Volume 43, Issue 1, February 2005
Medical and Biological Engineering and Computing : Volume 42
Medical and Biological Engineering and Computing : Volume 41
Medical and Biological Engineering and Computing : Volume 40
Medical and Biological Engineering and Computing : Volume 39
Medical and Biological Engineering and Computing : Volume 38
Medical and Biological Engineering and Computing : Volume 37
Medical and Biological Engineering and Computing : Volume 36
Medical and Biological Engineering and Computing : Volume 35

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Reduction of noise from magnetoencephalography data

Content Provider SpringerLink
Author Okawa, S. Honda, S.
Copyright Year 2005
Abstract A noise reduction method for magnetoencephalography (MEG) data is proposed. The method is a combination of Kalman filtering and factor analysis. A statespace model for a Kalman filter was constructed using the forward problem in MEG measurement. Factor analysis provide estimations of noise covariances required by the Kalman filter to eliminate independent additive sensor noise. The proposed method supports independent component analysis (ICA), which is difficult to use in MEG analysis owing to the sensor noise. Numerical experiments were conducted to investigate the performance of the proposed method. In a single dipole case where the maximum signal-to-noise ratio (SNR) was — 10 dB, approximately equivalent to raw MEG data, noise-free signals were successfully estimated from noisy data; a 0.02 s delay of the peak latency and 15–40% of attenuation of the peak amplitude were observed. Moreover, in a multiple dipole case, independent components preprocessed with the proposed method had high correlation, 0.88 at the lowest, with correlation of 0.69 and 0.52 for those preprocessed with conventional bandpass filters. The results show that the noise reduction method reduces sensor noise effectively. High SNR-independent components are obtained by the proposed method. Real MEG data analysis was also demonstrated. The proposed method extracted auditory evoked responses from unaveraged single-trial data.
Starting Page 630
Ending Page 637
Page Count 8
File Format PDF
ISSN 01400118
Journal Medical and Biological Engineering and Computing
Volume Number 43
Issue Number 5
e-ISSN 17410444
Language English
Publisher Springer-Verlag
Publisher Date 2005-01-01
Publisher Place Berlin, Heidelberg
Access Restriction One Nation One Subscription (ONOS)
Subject Keyword Kalman filter Factor analysis Independent component analysis Magnetoencephalography Human Physiology Computer Applications Neurosciences Imaging Radiology Biomedical Engineering
Content Type Text
Resource Type Article
Subject Biomedical Engineering Computer Science Applications
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