TY - CHAP
T1 - Time and Frequency Domain Features Extraction Comparison for Motor Imagery Detection
AU - Orjuela-Cañón, Alvaro D.
AU - Archila, Juan Sebastian Ramírez
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021/2/26
Y1 - 2021/2/26
N2 - 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.
AB - 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.
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U2 - 10.1007/978-3-030-69774-7_6
DO - 10.1007/978-3-030-69774-7_6
M3 - Chapter (peer-reviewed)
AN - SCOPUS:85103262559
SN - 9783030697730
T3 - Communications in Computer and Information Science
SP - 77
EP - 87
BT - Applications of Computational Intelligence
A2 - Orjuela-Cañón, Alvaro David
A2 - Lopez, Jesus
A2 - Arias-Londoño, Julián David
A2 - Figueroa-García, Juan Carlos
PB - Springer Science and Business Media Deutschland GmbH
T2 - 3rd IEEE Colombian Conference on Applications of Computational Intelligence, IEEE ColCACI 2020
Y2 - 7 August 2020 through 8 August 2020
ER -