Decoding Lower Limb Motor Attempts from EEG Signals in a Spinal Cord Injury Patient with Epidural Electrical Implant

Epidural electrical stimulation (EES) has been employed to restore motor functions in the lower limbs of patients with spinal cord injuries (SCIs). However, non-invasive methods for controlling this stimulation have not yet been explored. This study aims to evaluate various classification approaches to distinguish movements as a preparatory step for controlling walking in SCI patients with an epidural electrical implant. Four different classification techniques were tested on electroencephalographic (EEG) data analyzed in both time and frequency domains to decode movements from rest condition. The classification algorithm with the highest accuracy (79 ± 3% averaged across tasks) was then used to classify signals with EES activated, revealing that stimulation does not significantly impact classification performance (EES Off 81 ± 4% and EES On 80 ± 3%). Our research represents a first step towards developing a non-invasive closed-loop system to control EES in SCI patients.