Two Different Approaches of Feature Extraction for Classifying the EEG Signals

Jahankhani, Pari and Lara Torralbo, Juan Alfonso and Pérez Pérez, Aurora and Caraça-Valente Hernández, Juan Pedro (2011). Two Different Approaches of Feature Extraction for Classifying the EEG Signals. In: "12th International Conference on Engineering Applications of Neural Networks, EANN 2011, and the 7th IFIP WG 12.5 International Conference, AIAI 2011", 15/09/2010 - 18/09/2010, Corfu, Grecia. ISBN 978-3-642-23956-4. pp. 229-239. https://doi.org/10.1007/978-3-642-23957-1.

Description

Title: Two Different Approaches of Feature Extraction for Classifying the EEG Signals
Author/s:
  • Jahankhani, Pari
  • Lara Torralbo, Juan Alfonso
  • Pérez Pérez, Aurora
  • Caraça-Valente Hernández, Juan Pedro
Item Type: Presentation at Congress or Conference (Article)
Event Title: 12th International Conference on Engineering Applications of Neural Networks, EANN 2011, and the 7th IFIP WG 12.5 International Conference, AIAI 2011
Event Dates: 15/09/2010 - 18/09/2010
Event Location: Corfu, Grecia
Title of Book: Proceedings of the 12th International Conference on Engineering Applications of Neural Networks, EANN 2011, and the 7th IFIP WG 12.5 International Conference, AIAI 2011
Date: 2011
ISBN: 978-3-642-23956-4
Subjects:
Faculty: Facultad de Informática (UPM)
Department: Lenguajes y Sistemas Informáticos e Ingeniería del Software
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

The focus of this chapter is to study feature extraction and pattern classification methods from two medical areas, Stabilometry and Electroencephalography (EEG). Stabilometry is the branch of medicine responsible for examining balance in human beings. Balance and dizziness disorders are probably two of the most common illnesses that physicians have to deal with. In Stabilometry, the key nuggets of information in a time series signal are concentrated within definite time periods are known as events. In this chapter, two feature extraction schemes have been developed to identify and characterise the events in Stabilometry and EEG signals. Based on these extracted features, an Adaptive Fuzzy Inference Neural network has been applied for classification of Stabilometry and EEG signals.

More information

Item ID: 11540
DC Identifier: http://oa.upm.es/11540/
OAI Identifier: oai:oa.upm.es:11540
DOI: 10.1007/978-3-642-23957-1
Official URL: http://rd.springer.com/book/10.1007/978-3-642-23957-1/page/1
Deposited by: Memoria Investigacion
Deposited on: 23 Jul 2012 10:45
Last Modified: 20 Apr 2016 19:35
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