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Abstract:
If we adopt the simplified view that the brain is a
deterministic system having EEG as output, with isolated task
events as input, then we can use average event-related potentials
(ERPs) to approximate impulse response functions. In an actual
experiment, however, task events are not isolated. Rather, they
interact via brain memory systems, and their associated
electrophysiological responses often overlap in time. Consequently,
an average ERP may obscure the response dynamics. Moreover, an
average does not capture physiological interactions between events
that may be of interest in a cognitive experiment. As an
alternative to averaging, we demonstrate the feasibility of using
linear/nonlinear system identification methods to characterize
deterministic event-related dynamics. The method requires
continuous EEG data with task variables, and it produces estimates
of both linear responses and nonlinear interactions, which
characterize a Volterra system model. Each task variable has an
associated linear impulse response waveform, that is, a temporally
deconvolved ERP. Matrix-like kernels represent nonlinear
interactions for each pair of task variables. In the context of the
general linear model, Friston et al. (1998, Magn Reson Med
39:41-52) have applied a similar approach to fMRI time series.
Thus, dynamic system parameter estimation provides a common
framework for processing both ERP and event-related fMRI
experiments. We describe a simulation study that speaks to
limitations of the method, potential pitfalls, and conditions for
avoiding the latter.
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