Two Different Approaches of Feature Extraction for Classifying the EEG Signals

Jahankhani, Pari and Pérez Pérez, Aurora and Lara Torralbo, Juan Alfonso and Caraça-Valente Hernández, Juan Pedro (2011). Two Different Approaches of Feature Extraction for Classifying the EEG Signals. "IFIP Advances in Information and Communication Technology", v. 363/20 ; pp. 229-239. ISSN 1868-4238. https://doi.org/10.1007/978-3-642-23957-1_26.

Description

Title: Two Different Approaches of Feature Extraction for Classifying the EEG Signals
Author/s:
  • Jahankhani, Pari
  • Pérez Pérez, Aurora
  • Lara Torralbo, Juan Alfonso
  • Caraça-Valente Hernández, Juan Pedro
Item Type: Article
Título de Revista/Publicación: IFIP Advances in Information and Communication Technology
Date: September 2011
ISSN: 1868-4238
Volume: 363/20
Subjects:
Freetext Keywords: Wavelet – Feature extraction – EEG – Classifying
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 electroencephalograph (EEG) signal is one of the most widely used signals in the biomedicine field due to its rich information about human tasks. This research study describes a new approach based on i) build reference models from a set of time series, based on the analysis of the events that they contain, is suitable for domains where the relevant information is concentrated in specific regions of the time series, known as events. In order to deal with events, each event is characterized by a set of attributes. ii) Discrete wavelet transform to the EEG data in order to extract temporal information in the form of changes in the frequency domain over time- that is they are able to extract non-stationary signals embedded in the noisy background of the human brain. The performance of the model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed scheme has potential in classifying the EEG signals.

More information

Item ID: 11507
DC Identifier: http://oa.upm.es/11507/
OAI Identifier: oai:oa.upm.es:11507
DOI: 10.1007/978-3-642-23957-1_26
Official URL: http://www.springerlink.com/content/14l27466214k8460/
Deposited by: Memoria Investigacion
Deposited on: 25 Jul 2012 09:12
Last Modified: 20 Apr 2016 19:33
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