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 -