A general linear model for MEG beamformer imaging

Matthew J Brookes, Andrew M Gibson, Stephen D Hall, Paul Lawrence Furlong, Gareth R Barnes, Arjan Hillebrand, Krish D Singh, Ian E Holliday, Sue T Francis, Peter G Morris

Research output: Contribution to journalArticleAcademicpeer-review

45 Citations (Scopus)


A new general linear model (GLM) beamformer method is described for processing magnetoencephalography (MEG) data. A standard nonlinear beamformer is used to determine the time course of neuronal activation for each point in a predefined source space. A Hilbert transform gives the envelope of oscillatory activity at each location in any chosen frequency band (not necessary in the case of sustained (DC) fields), enabling the general linear model to be applied and a volumetric T statistic image to be determined. The new method is illustrated by a two-source simulation (sustained field and 20 Hz) and is shown to provide accurate localization. The method is also shown to locate accurately the increasing and decreasing gamma activities to the temporal and frontal lobes, respectively, in the case of a scintillating scotoma. The new method brings the advantages of the general linear model to the analysis of MEG data and should prove useful for the localization of changing patterns of activity across all frequency ranges including DC (sustained fields).

Original languageEnglish
Pages (from-to)936-46
Number of pages11
Issue number3
Publication statusPublished - Nov 2004
Externally publishedYes


  • Algorithms
  • Computer Simulation
  • Data Collection
  • False Positive Reactions
  • Fourier Analysis
  • Humans
  • Image Processing, Computer-Assisted/statistics & numerical data
  • Linear Models
  • Magnetic Resonance Imaging
  • Magnetoencephalography/statistics & numerical data
  • Monte Carlo Method
  • Scotoma/physiopathology

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