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Extraction of functional information from ongoing brain electrical activity: Extraction en temps-réel d'informations fonctionnelles à partir de l'activité électrique cérébrale

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons75278

Besserve,  M
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Besserve, M. (2011). Extraction of functional information from ongoing brain electrical activity: Extraction en temps-réel d'informations fonctionnelles à partir de l'activité électrique cérébrale. IRBM, 32(1), 27-34. doi:10.1016/j.irbm.2011.01.001.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-BC88-3
Zusammenfassung
The modern analysis of multivariate electrical brain signals requires advanced statistical tools to automatically extract and quantify their information content. These tools include machine learning techniques and information theory. They are currently used both in basic neuroscience and challenging applications such as brain computer interfaces. We review here how these methods have been used at the Laboratoire d’Électroencéphalographie et de Neurophysiologie Appliquée (LENA) to develop a general tool for the real time analysis of functional brain signals. We then give some perspectives on how these tools can help understanding the biological mechanisms of information processing.