Hybrid Encoder-Decoder Deep Networks for Decoding Neural Drive Information towards Precise Muscle Force Estimation.

Abstract

How to utilize and interpret microscopic motor unit (MU) activities after surface electromyogram (sEMG) decomposition towards accurate decoding of the neural control remains a great challenge. In this study, a novel framework of hybrid encoder-decoder deep networks is proposed to process the microscopic neural drive information and it is applied to precise muscle force estimation. After a high-density sEMG (HD-sEMG) decomposition was performed using the progressive FastICA peel-off algorithm, a muscle twitch force model was then applied to basically convert each channel's electric waveform (i.e., action potential) of each MU into a twitch force. Next, hybrid encoder-decoder deep networks were performed on every 50 ms of segment of the summation of twitch force trains from all decomposed MUs. The encoder network was designed to characterize spatial information of MU's force contribution over all channels, and the decoder network finally decoded the muscle force. This framework was validated on HD-sEMG recordings from the abductor pollicis brevis muscles of five subjects by a thumb abduction task using an 8 × 8 grid. The proposed framework yielded a mean root mean square error of 6.62% ± 1.26% and a mean coefficient of determination value of 0.95 ± 0.03 from a linear regression analysis between the estimated force and actual force over all data trials, and it outperformed three common methods with statistical significance (p < 0.001). This study offers a valuable solution for interpreting microscopic neural drive information and demonstrates its success in predicting muscle force.