Deciphering Brain Activity for Motor Imagery Through a Brain-Computer Interface

Published on September 2, 2022

Imagine sending commands to your legs just by thinking! In this study, scientists developed a brain-computer interface (BCI) that can interpret brain signals during motor imagery tasks. The goal was to create an effective BCI system for the actions of standing and sitting. By using advanced techniques like filter bank common spatial pattern (FBCSP) and regularized linear discriminant analysis (RLDA), the researchers were able to accurately decode EEG rhythms both offline and online. In offline analysis, the classification accuracy for motor imagery was around 88% for sit-to-stand and 85% for stand-to-sit transitions. The online experiments showed even higher accuracy, with about 95% accuracy for sit-to-stand and 97% accuracy for stand-to-sit transitions. These impressive results suggest that the MI-based BCI has great potential in developing brain-controlled standing systems in the future!

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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