Equivalent current dipoles are a powerful tool for modeling focal sources.
The dipole is often sufficient to adequately represent sources of measured
scalp potentials, even when the area of activation exceeds 1 cm(2) of corte
x. Traditional least-squares fitting techniques involve minimization of an
error function with respect to the location and orientation of the dipoles.
The existence of multiple local minima in this error function can result i
n gross errors in the computed source locations. The problem is further com
pounded by the requirement that the model order, i.e. the number of dipoles
, be determined before error minimization can be performed. An incorrect mo
del order can produce additional errors in the estimated source parameters.
Both of these problems can be avoided using alternative search strategies
based on the MUSIC (multiple signal classification) algorithm. Here the aut
hors review the MUSIC approach and demonstrate its application to the local
ization of multiple current dipoles from EEG data. The authors also show th
at the number of detectable sources can be determined in a recursive manner
from the data. Also, in contrast to least-squares, the method can find dip
olar sources in the presence of additional non-dipolar sources. Finally, ex
tensions of the MUSIC approach to allow the modeling of distributed sources
are discussed.