Sensors 2016, 16(4), 590; https://doi.org/10.3390/s16040590
Unsupervised Event Characterization and Detection in Multichannel Signals: An EEG application
1
Department of Computer Sciences and Automatic Control, UNED, Juan del Rosal 16, 28040 Madrid, Spain
2
National Fusion Laboratory by Magnetic Confinement, CIEMAT, Complutense 40, 28040 Madrid, Spain
*
Author to whom correspondence should be addressed.
Academic Editors: Steffen Leonhardt and Daniel Teichmann
Received: 4 March 2016 / Revised: 15 April 2016 / Accepted: 21 April 2016 / Published: 23 April 2016
(This article belongs to the Special Issue Noninvasive Biomedical Sensors)
Abstract
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. View Full-Text
Figure 1
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Mur, A.; Dormido, R.; Vega, J.; Duro, N.; Dormido-Canto, S. Unsupervised Event Characterization and Detection in Multichannel Signals: An EEG application. Sensors 2016, 16, 590.
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