Random Dipoles as a Model for Spontaneous EEG- and MEG-Activity
A serious obstacle for the interpretation of evoked electromagnetic fields is the presence of noise in the data. The background EEG is considered as the major origin of noise for evoked potentials. Also for evoked magnetic fields (EMF) the spontaneous brain activity is an important factor (Knuutila and Hämäläinen, 1987). When the statistical properties of this noise are known, confidence limits of estimated (dipole) parameters can be calculated (Sarvas, 1987) and noise filters can be designed to improve the signal to noise ratio. For this purpose we studied the spontaneous brain activity and formulated a mathematical model, which predicts the statistics of the electromagnetic field of the brain. Since the model gives the spatial covariance matrix in analytic form, this matrix needs not to be estimated every time an EMF is recorded.
KeywordsVolume Conductor Electrode Distance Spontaneous Brain Activity Spatial Covariance Matrix Stochastic Magnetic Induction
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