Fast dynamic time warping feature extraction for EEG signal classification

Hiram Calvo, Jose Luis Paredes, Jesus Figueroa Nazuno

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

In this work the fast algorithm Dynamic Time Warp (FDTW) is used as a method of feature extraction for 18 sets of EEG records. Each set contains 150 events of stimulation designed to study the semantic relationship between pairs of nouns of concrete objects such as "HORSE - SHEEP" and "SWING - MELON" and how this relationship activity is reflected in EEG signals. Based on these latter, different classifiers were trained in order to associate a set of signals to a previously learned human answer, pertaining to two classes: semantically related, or not semantically related. The results of classification accuracy were evaluated comparing with other 3 methods of feature extraction, and using 5 different classification algorithms.

Idioma originalInglés
Título de la publicación alojadaProceedings of a Special Session - 15th Mexican International Conference on Artificial Intelligence
Subtítulo de la publicación alojadaAdvances in Artificial Intelligence, MICAI 2016
EditoresGrigori Sidorov, Oscar Herrera Alcantara, Sabino Miranda Jimenez, Obdulia Pichardo Lagunas
EditorialInstitute of Electrical and Electronics Engineers Inc.
Páginas146-151
Número de páginas6
ISBN (versión digital)9781538677353
DOI
EstadoPublicada - 2016
Evento15th Mexican International Conference on Artificial Intelligence, MICAI 2016 - Cancun, Quintana Roo, México
Duración: 23 oct. 201629 oct. 2016

Serie de la publicación

NombreProceedings of a Special Session - 15th Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence, MICAI 2016

Conferencia

Conferencia15th Mexican International Conference on Artificial Intelligence, MICAI 2016
País/TerritorioMéxico
CiudadCancun, Quintana Roo
Período23/10/1629/10/16

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