Decoding EEG rhythms offline and online during motor imagery for standing and sitting based on a brain-computer interface

Nayid Triana-Guzman; Alvaro D Orjuela-Cañon; Andres L Jutinico; Omar Mendoza-Montoya; Javier M Antelis

doi:10.3389/fninf.2022.961089

Decoding EEG rhythms offline and online during motor imagery for standing and sitting based on a brain-computer interface

Front Neuroinform. 2022 Sep 2:16:961089. doi: 10.3389/fninf.2022.961089. eCollection 2022.

Authors

Nayid Triana-Guzman¹, Alvaro D Orjuela-Cañon², Andres L Jutinico³, Omar Mendoza-Montoya⁴, Javier M Antelis⁴

Affiliations

¹ Doctorado en Ciencia Aplicada, Universidad Antonio Nariño, Bogota, Colombia.
² School of Medicine and Health Sciences, Universidad del Rosario, Bogota, Colombia.
³ Facultad de Ingeniería Mecánica, Electrónica y Biomédica, Universidad Antonio Nariño, Bogota, Colombia.
⁴ Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Monterrey, Mexico.

Abstract

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.

Keywords: brain-computer interface (BCI); electroencephalogram (EEG); filter bank common spatial pattern (FBCSP); motor imagery (MI); online BCI; regularized linear discriminant analysis (RLDA); sit-stand.