Restoring Lower-Limb Movement eith a BCI-Controlled FES System

Who

Brian kang, Jayaditya

Background

Paralysis following spinal cord injuries limits motor function and independence. Traditional rehabilitation methods often lack precision, timing, or adaptability to patient intent.

What We Built

We developed a non-invasive BCI system that interprets EEG signals representing imagined leg movement and triggers functional electrical stimulation (FES) to contract specific muscles. The system closes the loop from brain activity to physical movement in real-time.

Modeling & Research

Using a dry-electrode EEG headset and Python-based signal classification, we trained a model on imagined dorsiflexion vs. rest states. Detected intent then activates an FES device pre-calibrated to ankle/knee muscle groups. Signal-to-stim latency was reduced to under 1.2 seconds.

Impact

This work advances the concept of non-invasive neural rehabilitation tools, opening the door to low-cost, portable neuro-rehab solutions for post-injury patients.

Outcome

Presented in independent research forums. Currently in expansion phase under NSRI's neuroscience division.