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Detecting Pre-Stimulus Source-Level Effects on Object Perception with Magnetoencephalography
09:25

Detecting Pre-Stimulus Source-Level Effects on Object Perception with Magnetoencephalography

Published on: July 26, 2019

GENERALIZED SIDELOBE CANCELLER FOR MAGNETOENCEPHALOGRAPHY ARRAYS.

John C Mosher1, Matti S Hämäläinen, Dimitrios Pantazis

  • 1Cleveland Clinic Neurological Institute, Epilepsy Center, Cleveland, OH USA.

Proceedings. IEEE International Symposium on Biomedical Imaging
|March 18, 2010
PubMed
Summary

We present a new noise cancellation method, the generalized sidelobe canceller (GSC), for large sensor arrays in magnetoencephalography (MEG) and electroencephalography (EEG). This approach effectively reduces noise and improves signal estimation in complex data.

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Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
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Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Large sensor arrays in magnetoencephalography (MEG) and electroencephalography (EEG) are increasingly common.
  • Beamforming techniques offer opportunities for signal estimation and noise reduction in neuroimaging.
  • Existing noise reduction methods include the linearly constrained minimum variance (LCMV) algorithm.

Purpose of the Study:

  • To introduce a novel noise cancellation approach for large sensor arrays.
  • To present the generalized sidelobe canceller (GSC) as an alternative to LCMV.
  • To demonstrate a practical noise filtering sequence for MEG/EEG data.

Main Methods:

  • Developed and applied the generalized sidelobe canceller (GSC) framework.
  • Integrated GSC with real or virtual reference arrays for noise reduction.
  • Utilized human subject data with significant environmental and biological artifacts for testing.

Main Results:

  • The GSC approach was successfully demonstrated for noise filtering in large sensor arrays.
  • The method proved effective in reducing artifacts in MEG/EEG data.
  • A straightforward sequence for practical noise filtering was established.

Conclusions:

  • The generalized sidelobe canceller (GSC) offers a viable alternative for noise cancellation in MEG and EEG.
  • The proposed method is applicable to various large array sensor designs.
  • This technique enhances signal estimation and noise reduction capabilities in neuroimaging.