Two Different Approaches of Feature Extraction for Classifying the EEG Signals

Jahankhani, Pari; Pérez Pérez, Aurora; Lara Torralbo, Juan Alfonso y 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.

Descripción

Título: Two Different Approaches of Feature Extraction for Classifying the EEG Signals
Autor/es:
  • Jahankhani, Pari
  • Pérez Pérez, Aurora
  • Lara Torralbo, Juan Alfonso
  • Caraça-Valente Hernández, Juan Pedro
Tipo de Documento: Artículo
Título de Revista/Publicación: IFIP Advances in Information and Communication Technology
Fecha: Septiembre 2011
Volumen: 363/20
Materias:
Palabras Clave Informales: Wavelet – Feature extraction – EEG – Classifying
Escuela: Facultad de Informática (UPM) [antigua denominación]
Departamento: Lenguajes y Sistemas Informáticos e Ingeniería del Software
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

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.

Más información

ID de Registro: 11507
Identificador DC: http://oa.upm.es/11507/
Identificador OAI: oai:oa.upm.es:11507
Identificador DOI: 10.1007/978-3-642-23957-1_26
URL Oficial: http://www.springerlink.com/content/14l27466214k8460/
Depositado por: Memoria Investigacion
Depositado el: 25 Jul 2012 09:12
Ultima Modificación: 20 Abr 2016 19:33
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