Machine learning techniques for detecting motor imagery in upper limbs

Juan Sebastian Ramirez Archila; Alvaro David Orjuela-Canon

doi:10.1109/ColCACI50549.2020.9247869

Machine learning techniques for detecting motor imagery in upper limbs

Juan Sebastian Ramirez Archila
, Alvaro David Orjuela-Canon

Research output: Chapter in Book/Inform › Conference contribution

1 Scopus citations

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.

Translated title of the contribution	Técnicas de aprendizaje automático para detectar imágenes motoras en miembros superiores
Original language	English (US)
Title of host publication	2020 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2020 - Proceedings
Editors	Alvaro David Orjuela-Canon
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1-5
Number of pages	5
ISBN (Electronic)	9781728194066
DOIs	https://doi.org/10.1109/ColCACI50549.2020.9247869
State	Published - Aug 7 2020
Event	2020 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2020 - Virtual, Cali, Colombia Duration: Aug 7 2020 → Aug 9 2020

Publication series

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

Conference

Conference	2020 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2020
Country/Territory	Colombia
City	Virtual, Cali
Period	8/7/20 → 8/9/20

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being
SDG 4 Quality Education

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

Access to Document

10.1109/ColCACI50549.2020.9247869

Cite this

Archila, J. S. R., & Orjuela-Canon, A. D. (2020). Machine learning techniques for detecting motor imagery in upper limbs. In A. D. Orjuela-Canon (Ed.), 2020 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2020 - Proceedings (pp. 1-5). Article 9247869 (2020 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2020 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ColCACI50549.2020.9247869

Archila, Juan Sebastian Ramirez ; Orjuela-Canon, Alvaro David. / Machine learning techniques for detecting motor imagery in upper limbs. 2020 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2020 - Proceedings. editor / Alvaro David Orjuela-Canon. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 1-5 (2020 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2020 - Proceedings).

@inproceedings{41783320301e49e49b1bf8877cf81185,

title = "Machine learning techniques for detecting motor imagery in upper limbs",

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. ",

author = "Archila, \{Juan Sebastian Ramirez\} and Orjuela-Canon, \{Alvaro David\}",

note = "Funding Information: Authors thank the Universidad del Rosario for the support in this work and the Semillero en Inteligencia Artificial en Salud - Semill-IAS. Also, the Universidad Antonio Nari?o developed an important role for the signals acquisition which can be devised this work. Publisher Copyright: {\textcopyright} 2020 IEEE. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 2020 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2020 ; Conference date: 07-08-2020 Through 09-08-2020",

year = "2020",

month = aug,

day = "7",

doi = "10.1109/ColCACI50549.2020.9247869",

language = "English (US)",

series = "2020 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2020 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1--5",

editor = "Orjuela-Canon, \{Alvaro David\}",

booktitle = "2020 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2020 - Proceedings",

address = "United States",

}

Archila, JSR & Orjuela-Canon, AD 2020, Machine learning techniques for detecting motor imagery in upper limbs. in AD Orjuela-Canon (ed.), 2020 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2020 - Proceedings., 9247869, 2020 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2020 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 1-5, 2020 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2020, Virtual, Cali, Colombia, 8/7/20. https://doi.org/10.1109/ColCACI50549.2020.9247869

Machine learning techniques for detecting motor imagery in upper limbs. / Archila, Juan Sebastian Ramirez; Orjuela-Canon, Alvaro David.
2020 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2020 - Proceedings. ed. / Alvaro David Orjuela-Canon. Institute of Electrical and Electronics Engineers Inc., 2020. p. 1-5 9247869 (2020 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2020 - Proceedings).

Research output: Chapter in Book/Inform › Conference contribution

TY - GEN

T1 - Machine learning techniques for detecting motor imagery in upper limbs

AU - Archila, Juan Sebastian Ramirez

AU - Orjuela-Canon, Alvaro David

N1 - Funding Information: Authors thank the Universidad del Rosario for the support in this work and the Semillero en Inteligencia Artificial en Salud - Semill-IAS. Also, the Universidad Antonio Nari?o developed an important role for the signals acquisition which can be devised this work. Publisher Copyright: © 2020 IEEE. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.

PY - 2020/8/7

Y1 - 2020/8/7

N2 - 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.

AB - 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.

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U2 - 10.1109/ColCACI50549.2020.9247869

DO - 10.1109/ColCACI50549.2020.9247869

M3 - Conference contribution

AN - SCOPUS:85097568278

T3 - 2020 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2020 - Proceedings

SP - 1

EP - 5

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

A2 - Orjuela-Canon, Alvaro David

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2020 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2020

Y2 - 7 August 2020 through 9 August 2020

ER -

Archila JSR, Orjuela-Canon AD. Machine learning techniques for detecting motor imagery in upper limbs. In Orjuela-Canon AD, editor, 2020 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2020. p. 1-5. 9247869. (2020 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2020 - Proceedings). doi: 10.1109/ColCACI50549.2020.9247869

Machine learning techniques for detecting motor imagery in upper limbs

Abstract

Publication series

Conference

UN SDGs

All Science Journal Classification (ASJC) codes

Access to Document

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