A data-driven machine learning approach for brain-computer interfaces targeting lower limb neuroprosthetics.

ABSTRACT: Prosthetic devices that replace a lost limb have become increasingly performant in recent years. Recent advances in both software and hardware allow for the decoding of electroencephalogram (EEG) signals to improve the control of active prostheses with brain-computer interfaces (BCI). Most BCI research is focused on the upper body. Although BCI research for the lower extremities has increased in recent years, there are still gaps in our knowledge of the neural patterns associated with lower limb movement. Therefore, the main objective of this study is to show the feasibility of decoding lower limb movements from EEG data recordings. The second aim is to investigate whether well-known neuroplastic adaptations in individuals with an amputation have an influence on decoding performance. To ad

SUBMITTER: Dillen A 

PROVIDER: S-EPMC9364873 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature