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One major challenge in neuroscience is the identification of interrelations between signals reflecting neural activity. When applying multivariate time series analysis techniques to neural signals, detection of directed relationships, which can be described in terms of Granger-causality, is of particular interest. Partial directed coherence has been(More)
Nonlinear time series analysis techniques have been proposed to detect changes in the electroencephalography dynamics prior to epileptic seizures. Their applicability in practice to predict seizure onsets is hampered by the present lack of generally accepted standards to assess their performance. We propose an analytic approach to judge the prediction(More)
Over the last decades several techniques have been developed to analyze interactions in multivariate dynamic systems. These analysis techniques have been applied to empirical data recorded in various branches of research, ranging from economics to biomedical sciences. Investigations of interactions between different brain structures are of strong interest(More)
Partial directed coherence is a powerful tool used to analyze interdependencies in multivariate systems based on vector autoregressive modeling. This frequency domain measure for Granger-causality is designed such that it is normalized to [0,1]. This normalization induces several pitfalls for the interpretability of the ordinary partial directed coherence,(More)
Cognitive functions are organized in distributed, overlapping, and interacting brain networks. Investigation of those large-scale brain networks is a major task in neuroimaging research. Here, we introduce a novel combination of functional and anatomical connectivity to study the network topology subserving a cognitive function of interest. (i) In a given(More)
PURPOSE Available seizure-prediction algorithms are accompanied by high numbers of false predictions to achieve high sensitivity. Little is known about the extent to which changes in EEG dynamics contribute to false predictions. This study addresses potential causes and the circadian distribution of false predictions as well as their relation to the(More)
BACKGROUND In patients with parkinsonian resting tremor, tremor-correlated activity in the contralateral sensorimotor cortex has been studied by both magnetoencephalography (MEG) and electroencephalography (EEG). In essential tremor, MEG failed to detect cortical involvement. The objective of this study was to investigate whether EEG recording can reveal(More)
The problem of determining directional coupling between neuronal oscillators from their time series is addressed. We compare performance of the two well-established approaches: partial directed coherence and phase dynamics modeling. They represent linear and nonlinear time series analysis techniques, respectively. In numerical experiments, we found each of(More)
OBJECTIVE Coherence analysis of electromyography (EMG) signals in essential tremor (ET) suggests that tremor in the right and left arm is induced by independent central oscillators. The sensorimotor cortex seems to be part of the tremor-generating neuronal network in ET. Here, we investigated using electroencephalography (EEG) whether the independence of(More)
The retrospective identification of preseizure states usually bases on a time-resolved characterization of dynamical aspects of multichannel neurophysiologic recordings that can be assessed with measures from linear or non-linear time series analysis. This approach renders time profiles of a characterizing measure - so-called measure profiles - for(More)