Adaptive Beamformers - Electromagnetic Brain Imaging - page 49

The proposed weight is therefore derived in a recursive manner. That is, by setting an
initial

2 to have a uniform value, the weight

( , ) w ( )

s r t r is derived using Eqs. ( 3.94 )

and ( 3.96 ). The estimated source intensity s ( r , t ) is obtained using Eq. ( 3.1 ). This

s . These procedures are

repeated until some stopping criterion is satisfied. Here we describe a derivation of
the scalar-type RENS beamformer. An extension for deriving the vector-type RENS
beamformer is straightforward [ 18 ].
The RENS beamformer described here provides spatial resolution higher than
that of the nonadaptive spatial filters described in Sect. 3.8 . Moreover, it is free from
the limitations of adaptive beamformers. That is, the RENS beamformer is robust
to the source correlation problem. It does not require large time samples and works
even with single time point data.

( r , t ) is then used in Eqs. ( 3.94 ) and ( 3.96 ) to update w ( r )

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