Christoph Dinh

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With its millisecond temporal resolution, Magnetoencephalography (MEG) is well suited for real-time monitoring of brain activity. Real-time feedback allows the adaption of the experiment to the subject’s reaction and increases time efficiency by shortening acquisition and off-line analysis. Two formidable challenges exist in real-time analysis: the low(More)
Nowadays, advanced sensing technologies are used in many scientific and engineering disciplines, e. g., in medical or industrial applications, enabling the usage of data-driven techniques to derive models. Measures are collected, filtered, aggregated, and processed in a complex analytic pipeline, joining them with static models to perform high-level tasks(More)
Magnetoencephalography (MEG) and electroencephalography provide a high temporal resolution, which allows estimation of the detailed time courses of neuronal activity. However, in real-time analysis of these data two major challenges must be handled: the low signal-to-noise ratio (SNR) and the limited time available for computations. In this work, we present(More)
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