Reduction of noise from magnetoencephalography data

Shinpei Okawa, S. Honda

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

A noise reduction method for magnetoencephalography (MEG) data is proposed. The method is a combination of Kalman filtering and factor analysis. A state-space 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.

Original languageEnglish
Pages (from-to)630-637
Number of pages8
JournalMedical and Biological Engineering and Computing
Volume43
Issue number5
DOIs
Publication statusPublished - 2005 Sep

Fingerprint

Magnetoencephalography
Acoustic noise
Noise
Factor analysis
Noise abatement
Kalman filters
Signal to noise ratio
Sensors
Signal-To-Noise Ratio
Statistical Factor Analysis
Independent component analysis
Bandpass filters
Space Simulation
Auditory Evoked Potentials
Experiments

Keywords

  • Factor analysis
  • Independent component analysis
  • Kalman filter
  • Magneto-encephalography

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management
  • Computer Science Applications
  • Computational Theory and Mathematics

Cite this

Reduction of noise from magnetoencephalography data. / Okawa, Shinpei; Honda, S.

In: Medical and Biological Engineering and Computing, Vol. 43, No. 5, 09.2005, p. 630-637.

Research output: Contribution to journalArticle

Okawa, Shinpei ; Honda, S. / Reduction of noise from magnetoencephalography data. In: Medical and Biological Engineering and Computing. 2005 ; Vol. 43, No. 5. pp. 630-637.
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