Next Article in Journal
Healthcare4VideoStorm: Making Smart Decisions Based on Storm Metrics
Next Article in Special Issue
Unobtrusive Estimation of Cardiac Contractility and Stroke Volume Changes Using Ballistocardiogram Measurements on a High Bandwidth Force Plate
Previous Article in Journal
Electromyographic Patterns during Golf Swing: Activation Sequence Profiling and Prediction of Shot Effectiveness
Previous Article in Special Issue
Sensor Monitoring of Physical Activity to Improve Glucose Management in Diabetic Patients: A Review
Article Menu

Export Article

Open AccessArticle
Sensors 2016, 16(4), 590;

Unsupervised Event Characterization and Detection in Multichannel Signals: An EEG application

Department of Computer Sciences and Automatic Control, UNED, Juan del Rosal 16, 28040 Madrid, Spain
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)
Full-Text   |   PDF [2381 KB, uploaded 23 April 2016]   |  


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
Keywords: artifacts; EEG; event characterization; event detection; unsupervised classification artifacts; EEG; event characterization; event detection; unsupervised classification

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top