Application of an MEG eigenspace beamformer to reconstructing spatio-temporal activities of neural sources

Kensuke Sekihara, Srikantan S. Nagarajan, David Poeppel, Alec Marantz, Yasushi Miyashita

Research output: Contribution to journalArticlepeer-review


We have applied the eigenspace-based beamformer to reconstruct spatio-temporal activities of neural sources from MEG data. The weight vector of the eigenspace-based beamformer is obtained by projecting the weight vector of the minimum-variance beamformer onto the signal subspace of a measurement covariance matrix. This projection removes the residual noise-subspace component that considerably degrades the signal-to-noise ratio (SNR) of the beamformer output when errors in estimating the sensor lead field exist. Therefore, the eigenspace-based beamformer produces a SNR considerably higher than that of the minimum-variance beamformer in practical situations. The effectiveness of the eigenspace-based beamformer was validated in our numerical experiments and experiments using auditory responses. We further extended the eigenspace-based beamformer so that it incorporates the information regarding the noise covariance matrix. Such a prewhitened eigenspace beamformer was experimentally demonstrated to be useful when large background activity exists.

Original languageEnglish (US)
Pages (from-to)199-215
Number of pages17
JournalHuman Brain Mapping
Issue number4
StatePublished - 2002


  • Beamformer
  • Biomagnetism
  • Functional neuroimaging
  • MEG inverse problems
  • Magnetoencephalography
  • Neuromagnetic signal processing

ASJC Scopus subject areas

  • Anatomy
  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Neurology
  • Clinical Neurology


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