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The purpose of this paper is to study the performance of diffusion-based distributed adaptive algorithms when relaxing the static assumption on network topology. Adaptive networks with topologies that change across time are useful to model a wide class of real-time sensor networks. This includes topologies where the number of nodes is variable and links are dynamic. We propose two schemes to accommodate...
Phase synchrony is a powerful amplitudeindependent measure that quantifies linear and nonlinear dynamics between non-stationary signals. It has been widely used in a variety of disciplines including neural science and cognitive psychology. Current time-varying phase estimation uses either the Hilbert transform or the complex wavelet transform of the signals. This paper exploits the concept of phase...
In this paper, we propose a graph-theoretical approach to reveal patterns of functional dependencies between different scalp regions. We start by computing pairwise measures of dependence from dense-array scalp electroencephalographic (EEG) recordings. The obtained dependence matrices are then averaged over trials and further statistically processed to provide more reliability. Graph structure information...
The purpose of this paper is two-fold: first, to propose a modification to the generalized measure of association (GMA) framework that reduces the effect of temporal structure in time series; second, to assess the reliability of using association methods to capture dependence between pairs of EEG channels using their time series or envelopes. To achieve the first goal, the GMA algorithm was updated...
Communication between cortices mediated by deep brain structures such as the amygdala and fusiform gyrus has been suggested to explain the enhanced perception of stimuli bearing emotional content or having facial features. In this paper, we analyze the dependence structure of the relevant brain regions to assess their connectivity in response to a facial stimulus, and to discriminate it from a mock...
This paper addresses the robustness of the filtering schemes in processing high resolution electroencephalogram (EEG) data in the context of discriminating two stimuli flickering at a given frequency. The raw data consists of recordings from a 128-channel HydroCell GSN where the subject was visually stimulated with two images flickering at 17.5 Hz, representing two distinct conditions, referred to...
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