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Robust EEG time series transient detection with a momentary frequency estimator for the indication of an emotional change

  • This paper describes adaptive time frequency analysis of EEG signals, both in theory as well as in practice. A momentary frequency estimation algorithm is discussed and applied to EEG time series of test persons performing a concentration experiment. The motivation for deriving and implementing a time frequency estimator is the assumption that an emotional change implies a transient in the measured EEG time series, which again are superimposed by biological white noise as well as artifacts. It will be shown how accurately and robustly the estimator detects the transient even under such complicated conditions.

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Metadaten
Document Type:Conference Object
Language:English
Author:Gernot Heisenberg, Ramesh K. Natarajan, Yashar A. Rezaei, Nicolas Simon, Wolfgang Heiden
Parent Title (English):6th Workshop of Emotion and Computing at the 35th German Conference on Artificial Intelligence (KI 2012)
Publication year:2012
Keyword:EEG; adaptive filters; affective computing; brain computer interfaces; emotion computing; momentary frequency; time series processing
Departments, institutes and facilities:Fachbereich Informatik
Institute of Visual Computing (IVC)
Institut für funktionale Gen-Analytik (IFGA)
Dewey Decimal Classification (DDC):0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Entry in this database:2015/04/02