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Author(s) :
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Abstract :
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In electroneurophysiology, single-trial brain responses to a
sensory stimulus or a motor act are commonly assumed to result
from the linear superposition of a stereotypic event-related
signal (e.g. the event-related potential or ERP) that is invariant
across trials and some ongoing brain activity often referred to as
noise. To extract the signal, one performs an ensemble average of
the brain responses over many identical trials to attenuate the
noise. To date, this simple signal-plus-noise (SPN) model has been
the dominant approach in cognitive neuroscience. Mounting
empirical evidence has shown that the assumptions underlying this
model may be overly simplistic. More realistic models have been
proposed that account for the trial-to-trial variability of the
event-related signal as well as the possibility of multiple
differentially varying components within a given ERP waveform. The
variable-signal-plus-noise (VSPN) model, which has been
demonstrated to provide the foundation for separation and
characterization of multiple differentially varying components,
has the potential to provide a rich source of information for
questions related to neural functions that complement the SPN
model. Thus, being able to estimate the amplitude and latency of
each ERP component on a trial-by-trial basis provides a critical
link between the perceived benefits of the VSPN model and its many
concrete applications. In this paper we describe a Bayesian
approach to deal with this issue and the resulting strategy is
referred to as the differentially Variable Component Analysis
(dVCA). We compare the performance of dVCA on simulated data with
Independent Component Analysis (ICA) and analyze neurobiological
recordings from monkeys performing cognitive tasks.
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