TY - GEN
T1 - Detection of Pedaling Tasks through EEG Using Extreme Learning Machine for Lower-Limb Rehabilitation Brain-Computer Interfaces
AU - Blanco-Diaz, C. F.
AU - Guerrero-Mendez, C. D.
AU - Bastos-Filho, T. F.
AU - Ruiz-Olaya, A. F.
AU - Jaramillo-Isaza, S.
N1 - Funding Information:
The authors would like to thank the Federal University of Espírito Santo (UFES) for the support to this research, and FAPES/I2CA (Resolution N° 285/2021).
Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Brain-Computer Interfaces (BCI) are systems that may function as communication channels between people and external devices through brain information. BCIs using Electroencephalography (EEG) combined with robotic systems, such as minibikes, have enabled the rehabilitation of stroke patients by decoding their actions and executing a motor task. However, the Signal-To-Noise Ratio (SNR) of EEG is low, and there is intersubject variability for pedaling detection through brain information, which reduces the Accuracy of the rehabilitation devices. Additionally, in real-Time BCIs, it is necessary to maintain a good ratio of detection and execution times. In this work, it is proposed a methodology based on an Extreme Learning Machine (ELM) to identify when the subject is executing pedaling tasks through EEG, which allows efficient detection with a maximum Accuracy of 0.85 and a False Positive Rate of 0.07. Additionally, four different frequency bands in the filtering stage were evaluated, and the results allowed concluding that the most discriminant information was available between two frequency bands: 3-7 Hz and 7-13 Hz, with an area under the ROC curve average of 0.71. The results indicate that the proposed method is suitable for the detection of pedaling tasks using EEG, which could be used for the control of a real-Time BCI for lower-limb rehabilitation.
AB - Brain-Computer Interfaces (BCI) are systems that may function as communication channels between people and external devices through brain information. BCIs using Electroencephalography (EEG) combined with robotic systems, such as minibikes, have enabled the rehabilitation of stroke patients by decoding their actions and executing a motor task. However, the Signal-To-Noise Ratio (SNR) of EEG is low, and there is intersubject variability for pedaling detection through brain information, which reduces the Accuracy of the rehabilitation devices. Additionally, in real-Time BCIs, it is necessary to maintain a good ratio of detection and execution times. In this work, it is proposed a methodology based on an Extreme Learning Machine (ELM) to identify when the subject is executing pedaling tasks through EEG, which allows efficient detection with a maximum Accuracy of 0.85 and a False Positive Rate of 0.07. Additionally, four different frequency bands in the filtering stage were evaluated, and the results allowed concluding that the most discriminant information was available between two frequency bands: 3-7 Hz and 7-13 Hz, with an area under the ROC curve average of 0.71. The results indicate that the proposed method is suitable for the detection of pedaling tasks using EEG, which could be used for the control of a real-Time BCI for lower-limb rehabilitation.
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U2 - 10.1109/ColCACI59285.2023.10225911
DO - 10.1109/ColCACI59285.2023.10225911
M3 - Conference contribution
AN - SCOPUS:85171613344
T3 - 2023 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2023 - Proceedings
BT - 2023 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2023 - Proceedings
A2 - Orjuela-Canon, Alvaro David
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2023
Y2 - 26 July 2023 through 28 July 2023
ER -
