Abstract
Electromyographic (EMG) control of prosthetics is well established both in research and clinical settings. However, it remains unclear how much of the EMG information can be predicted from the
electroencephalographic (EEG) signals, and used instead, for control. In this study, we used a dataset that contains simultaneously acquired EEG and EMG signals of 31 subjects performing 33 grasping conditions, and applied unscented Kalman filtering (UKF) to continuously predict the EMG grasping envelopes
from the low-frequency (0.1-2 Hz) EEG. We achieved higher prediction accuracy for intermediate grasps compared to power or precision grasps. Our findings indicate the feasibility of continuously predicting EMG envelopes of grasping movements from EEG signals.
electroencephalographic (EEG) signals, and used instead, for control. In this study, we used a dataset that contains simultaneously acquired EEG and EMG signals of 31 subjects performing 33 grasping conditions, and applied unscented Kalman filtering (UKF) to continuously predict the EMG grasping envelopes
from the low-frequency (0.1-2 Hz) EEG. We achieved higher prediction accuracy for intermediate grasps compared to power or precision grasps. Our findings indicate the feasibility of continuously predicting EMG envelopes of grasping movements from EEG signals.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the Annual Meeting of the Austrian Society for Biomedical Engineering 2021 |
| Subtitle of host publication | ÖGBMT 2021 |
| Editors | Gernot Müller-Putz, Christian Baumgartner |
| Place of Publication | Graz |
| Publisher | Verlag der Technischen Universität Graz |
| Pages | 71-74 |
| ISBN (Electronic) | 978-3-85125-826-4 |
| DOIs | |
| Publication status | Published - 2021 |