Time and Frequency Domain Features Extraction Comparison for Motor Imagery Detection

Alvaro D. Orjuela-Cañón, Juan Sebastian Ramírez Archila

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review


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.

Original languageEnglish (US)
Title of host publicationApplications of Computational Intelligence
EditorsAlvaro David Orjuela-Cañón, Jesus Lopez, Julián David Arias-Londoño, Juan Carlos Figueroa-García
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages11
ISBN (Print)9783030697730
StatePublished - Feb 26 2021
Event3rd IEEE Colombian Conference on Applications of Computational Intelligence, IEEE ColCACI 2020 - Virtual, Online
Duration: Aug 7 2020Aug 8 2020

Publication series

NameCommunications in Computer and Information Science
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


Conference3rd IEEE Colombian Conference on Applications of Computational Intelligence, IEEE ColCACI 2020
CityVirtual, Online

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Mathematics(all)

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