TY - JOUR
T1 - Decoding EEG rhythms offline and online during motor imagery for standing and sitting based on a brain-computer interface
AU - Triana-Guzman, Nayid
AU - Orjuela-Cañon, Alvaro D.
AU - Jutinico, Andres L.
AU - Mendoza-Montoya, Omar
AU - Antelis, Javier M.
N1 - Publisher Copyright:
Copyright © 2022 Triana-Guzman, Orjuela-Cañon, Jutinico, Mendoza-Montoya and Antelis.
Copyright © 2022 Triana-Guzman, Orjuela-Cañon, Jutinico, Mendoza-Montoya and Antelis.
PY - 2022/9/2
Y1 - 2022/9/2
N2 - Motor imagery (MI)-based brain-computer interface (BCI) systems have shown promising advances for lower limb motor rehabilitation. The purpose of this study was to develop an MI-based BCI for the actions of standing and sitting. Thirty-two healthy subjects participated in the study using 17 active EEG electrodes. We used a combination of the filter bank common spatial pattern (FBCSP) method and the regularized linear discriminant analysis (RLDA) technique for decoding EEG rhythms offline and online during motor imagery for standing and sitting. The offline analysis indicated the classification of motor imagery and idle state provided a mean accuracy of 88.51 ± 1.43% and 85.29 ± 1.83% for the sit-to-stand and stand-to-sit transitions, respectively. The mean accuracies of the sit-to-stand and stand-to-sit online experiments were 94.69 ± 1.29% and 96.56 ± 0.83%, respectively. From these results, we believe that the MI-based BCI may be useful to future brain-controlled standing systems.
AB - Motor imagery (MI)-based brain-computer interface (BCI) systems have shown promising advances for lower limb motor rehabilitation. The purpose of this study was to develop an MI-based BCI for the actions of standing and sitting. Thirty-two healthy subjects participated in the study using 17 active EEG electrodes. We used a combination of the filter bank common spatial pattern (FBCSP) method and the regularized linear discriminant analysis (RLDA) technique for decoding EEG rhythms offline and online during motor imagery for standing and sitting. The offline analysis indicated the classification of motor imagery and idle state provided a mean accuracy of 88.51 ± 1.43% and 85.29 ± 1.83% for the sit-to-stand and stand-to-sit transitions, respectively. The mean accuracies of the sit-to-stand and stand-to-sit online experiments were 94.69 ± 1.29% and 96.56 ± 0.83%, respectively. From these results, we believe that the MI-based BCI may be useful to future brain-controlled standing systems.
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U2 - 10.3389/fninf.2022.961089
DO - 10.3389/fninf.2022.961089
M3 - Research Article
C2 - 36120085
AN - SCOPUS:85138173839
SN - 1662-5196
VL - 16
SP - 961089
JO - Frontiers in Neuroinformatics
JF - Frontiers in Neuroinformatics
M1 - 961089
ER -