Digital Signal Processing and System Theory

Talk Grant Davtjan

Identifizierung und Unterdrueckung von Artefakten in EEG-Signalen in Echtzeit

Date: 20.06.2013, 14:15 h - 15:00 h
Room: Aquarium

Grant Davtjan
CAU, Kiel, Germany,


It is well known that Electroencephalographic (EEG) recordings are widely used in neuroscience in the diagnosis and source localization of many neural disorders and phenomena, such as coma, epilepsy, and tremors. The recorded EEG signals are very often  contaminated with artifacts of physiological (e.g. eye blinking, eye movement, heart beating, and muscle artifacts) or technical (e.g. power-line artifact, electrode popping) origins. Usually,  such artifacts are detected by visual inspection. However, experience is needed in order to know how the artifacts look-like, and hence to mark where and when they appear. Moreover, this task might be timeconsuming, if many channels and many seconds have been recorded. In the case of EEG recordings from epilepsy patients, muscle artifacts are commonly present and contaminate the signals obscuring the desired information; therefore, an efficient filtering technique is necessary. The purpose of the work described in this thesis is to detect and suppress muscle artifacts from EEG data in an automatic manner and in real-time. To achieve that, basic pattern recognition algorithms have been implemented to detect the muscle artifacts. Once they are detected, the signals are enhanced using a Wiener-filter.