  In neuroelectrophysiology one records electric potentials or magnetic fields
generated by ensembles of synchronously active neurons in response to
externally presented stimuli. These evoked responses are often produced by
multiple generators in the presence of ongoing background activity. While
source localization techniques or current source density estimation are usually
used to identify generators, application of blind source separation techniques
to obtain independent components has become more popular.
  We approach this problem by applying the Bayesian methodology to a more
physiologically-realistic source model. As it is generally accepted that single
trials vary in amplitude and latency, we incorporate this variability into the
model. Rather than making the unrealistic assumption that these cortical
components are independent of one another, our algorithm utilizes the
differential amplitude and latency variability of the evoked waveforms to
identify the cortical components. The algorithm is applied to
intracortically-recorded local field potentials in monkeys performing a
visuomotor task.
