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