Deep Learning-Based Approaches for Decoding Motor Intent From Peripheral Nerve Signals.

ABSTRACT: Previous literature shows that deep learning is an effective tool to decode the motor intent from neural signals obtained from different parts of the nervous system. However, deep neural networks are often computationally complex and not feasible to work in real-time. Here we investigate different approaches' advantages and disadvantages to enhance the deep learning-based motor decoding paradigm's efficiency and inform its future implementation in real-time. Our data are recorded from the amputee's residual peripheral nerves. While the primary analysis is offline, the nerve data is cut using a sliding window to create a "pseudo-online" dataset that resembles the conditions in a real-time paradigm. First, a comprehensive collection of feature extraction techniques is applied to reduce the i

SUBMITTER: Luu DK 

PROVIDER: S-EPMC8260935 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature