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In this paper, EEG recordings on each channel are seen as non stationary time series. We assume a piecewise stationary model for the signal where the changes affect the power spectrum. Such a model is particularly relevant for analyzing Alzheimer disease (AD) dataset as the disease leads to variation of the frequency rhythms. Our method is devoted to the estimation of the partition. We define a criterion...
We explore the potential of transient local synchrony in EEG, as a marker for MCI (mild cognitive impairment). EEG signals of patients with MCI are transformed to a wavelet time-frequency representation, and afterwards a sparsification process (bump modeling) extracts time-frequency oscillatory bursts. We observed that organized oscillatory events contain stronger discriminative signatures than averaged...
Recovering the origin of an incident after detection of a polluting substance in the environment is crucial to start the remediation procedures. The lack of observations, the measurement errors and the model uncertainties make the problem of source estimation an ill-posed inverse problem that requires regularization to determine a solution. The two most frequent methods of regularization are source...
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