Time and Frequency Domain Features Extraction Comparison for Motor Imagery Detection

Alvaro D. Orjuela-Cañón, Juan Sebastian Ramírez Archila

Resultado de la investigación: Capítulo en Libro/Reporte/ConferenciaContribución a la conferencia


The brain computer interface area has increased the number of applications in the last years, searching to improve the quality of life in injured people. In spite of the progress in the field, different strategies are analyzed in order to contribute in specific problems related to the main applications. Present proposal shows a comparison between the use of time or frequency domain for feature extraction in upper limbs motor imagery. Four machine learning techniques as K-Nearest Neighbor, Support Vector Machine, Neural Networks and Random Forest were trained to detect motor imagery from EEG signals. Comparison for feature extraction and the employed detection models were analyzed to find the best election in an application for close-open fist in hands for two scenarios, according to two or three classes classification. The results achieved more than 90% in accuracy for both domain approaches in the two classes case. For the three classes detection, the results dropped out to 87% in accuracy. In general, the frequency domain is preferable for feature extraction and the KNN classifier was the best strategy for the present study.

Idioma originalInglés estadounidense
Título de la publicación alojadaApplications of Computational Intelligence - 3rd IEEE Colombian Conference, ColCACI 2020, Revised Selected Papers
EditoresAlvaro David Orjuela-Cañón, Jesus Lopez, Julián David Arias-Londoño, Juan Carlos Figueroa-García
EditorialSpringer Science and Business Media Deutschland GmbH
Número de páginas11
ISBN (versión impresa)9783030697730
EstadoPublicada - feb 26 2021
Evento3rd IEEE Colombian Conference on Applications of Computational Intelligence, IEEE ColCACI 2020 - Virtual, Online
Duración: ago 7 2020ago 8 2020

Serie de la publicación

NombreCommunications in Computer and Information Science
ISSN (versión impresa)1865-0929
ISSN (versión digital)1865-0937


Conferencia3rd IEEE Colombian Conference on Applications of Computational Intelligence, IEEE ColCACI 2020
CiudadVirtual, Online

All Science Journal Classification (ASJC) codes

  • Informática (todo)
  • Matemáticas (todo)

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