Fast dynamic time warping feature extraction for EEG signal classification

Hiram Calvo, Jose Luis Paredes, Jesus Figueroa Nazuno

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of a Special Session - 15th Mexican International Conference on Artificial Intelligence
Subtitle of host publicationAdvances in Artificial Intelligence, MICAI 2016
EditorsGrigori Sidorov, Oscar Herrera Alcantara, Sabino Miranda Jimenez, Obdulia Pichardo Lagunas
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages146-151
Number of pages6
ISBN (Electronic)9781538677353
DOIs
StatePublished - 2016
Event15th Mexican International Conference on Artificial Intelligence, MICAI 2016 - Cancun, Quintana Roo, Mexico
Duration: 23 Oct 201629 Oct 2016

Publication series

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

Conference

Conference15th Mexican International Conference on Artificial Intelligence, MICAI 2016
Country/TerritoryMexico
CityCancun, Quintana Roo
Period23/10/1629/10/16

Keywords

  • Classification
  • Electroencephalogram (EEG)
  • Emotiv EPOC
  • Fast Dynamic Time Warping (FDTW)
  • Oddball

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