RESUMO
The inference of interaction structures in multidimensional time series is a major challenge not only in neuroscience but in many fields of research. To gather information about the connectivity in a network from measured data, several parametric as well as non-parametric approaches have been proposed and widely examined. Today a lot of interest is focused on the evolution of the network connectivity in time which might contain information about ongoing tasks in the brain or possible dynamic dysfunctions. Therefore an extension of the current approaches towards time-resolved analysis techniques is desired. We present a parametric approach for time variant analysis, test its performance for simulated data, and apply it to real-world data.
Assuntos
Músculo Esquelético/fisiopatologia , Doença de Parkinson/fisiopatologia , Núcleo Subtalâmico/fisiopatologia , Tremor/fisiopatologia , Simulação por Computador , Eletroencefalografia , Eletromiografia , Antebraço/fisiopatologia , Humanos , Modelos Neurológicos , Rede Nervosa/fisiopatologia , Neurônios/fisiologia , Processamento de Sinais Assistido por Computador , Fatores de TempoRESUMO
Several procedures have been proposed to be capable of predicting the occurrence of epileptic seizures. Up to now, all proposed algorithms are far from being sufficient for a clinical application. This is, however, often not obvious when results of seizure prediction performance are reported. Here, we discuss impacts of long prediction horizons with respect to clinical needs and the strain on patients by analyzing long-term continuous intracranial electroencephalography data.
