Machine learning techniques for detecting motor imagery in upper limbs

Juan Sebastian Ramirez Archila, Alvaro David Orjuela-Canon

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Nowadays, the human machine interfaces have increased the applications for improving the quality of life in injured people. In spite of the progress in the field, new strategies are important to contribute to solve new problems. This proposal shows the employing of feature extraction in time and frequency domains. Three machine learning techniques as KNN, SVM and Random Forest were used 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. The results achieved more than 90% in accuracy for both approaches, showing as the frequency domain is preferable for feature extraction and the employment of the KNN classifier as best strategy for the present demand.

Original languageEnglish (US)
Title of host publication2020 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2020 - Proceedings
EditorsAlvaro David Orjuela-Canon
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-5
Number of pages5
ISBN (Electronic)9781728194066
DOIs
StatePublished - Aug 7 2020
Event2020 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2020 - Virtual, Cali, Colombia
Duration: Aug 7 2020Aug 9 2020

Publication series

Name2020 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2020 - Proceedings

Conference

Conference2020 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2020
CountryColombia
CityVirtual, Cali
Period8/7/208/9/20

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Decision Sciences (miscellaneous)
  • Information Systems and Management
  • Control and Optimization

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