Unsupervised Event Characterization and Detection in Multichannel Signals: An EEG application.
In this paper, we propose a new unsupervised method to automatically characterize and detect events in multichannel signals. This method is used to identify artifacts in electroencephalogram (EEG) recordings of brain activity. The proposed algorithm has been evaluated and compared with a supervised method. To this end an example of the performance of the algorithm to detect artifacts is shown. The results show that although both methods obtain similar classification, the proposed method allows detecting events without training data and can also be applied in signals whose events are unknown a priori. Furthermore, the proposed method provides an optimal window whereby an optimal detection and characterization of events is found. The detection of events can be applied in real-time.
Department of Computer Sciences and Automatic Control, UNED, Juan del Rosal 16, 28040 Madrid, Spain. a.r.m.g@outlook.fr.
Angel
A
Mur
Department of Computer Sciences and Automatic Control, UNED, Juan del Rosal 16, 28040 Madrid, Spain. raquel@dia.uned.es.
Raquel
R
Dormido
National Fusion Laboratory by Magnetic Confinement, CIEMAT, Complutense 40, 28040 Madrid, Spain. jesus.vega@ciemat.es.
Jesús
J
Vega
Department of Computer Sciences and Automatic Control, UNED, Juan del Rosal 16, 28040 Madrid, Spain. nduro@dia.uned.es.
Natividad
N
Duro
Department of Computer Sciences and Automatic Control, UNED, Juan del Rosal 16, 28040 Madrid, Spain. sebas@dia.uned.es.
Sebastian
S
Dormido-Canto
D000465:Algorithms; D016477:Artifacts; D004569:Electroencephalography; D006801:Humans; D012815:Signal Processing, Computer-Assisted
EEG; artifacts; event characterization; event detection; unsupervised classification