Abstract
Decades of research determined electromyography (EMG) and electroencephalography (EEG), individually, as paramount controls aimed at rehabilitation. However, correlating commands from the central nervous system to lower-limb movements are highly complex raising challenges for anthropomorphic lower-limb prosthesis control. This work establishes a hierarchical relationship between 12-channel EEG and 4-channel surface EMG using a hybrid model (LRG-2L-LSTM) for lower-limb ankle movement recognition by estimating muscular activity from cortical brain signals. The proposed model achieved R 2 = 0.742 ± 0.03, RMSE = 0.067 ± 0.002 for EMG estimation and averaged recognition accuracy of 84.86 ± 0.27% for ankle movements using estimated EMG, thereby, establishing lower-limb prosthesis and exoskeleton control for amputees with little to no muscular strength.
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