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Related Experiment Video

Updated: Sep 1, 2025

Engineering Platform and Experimental Protocol for Design and Evaluation of a Neurally-controlled Powered Transfemoral Prosthesis

Engineering Platform and Experimental Protocol for Design and Evaluation of a Neurally-controlled Powered Transfemoral Prosthesis

Published on: July 22, 2014

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

Arnau Dillen^1,2,3, Elke Lathouwers^1,2, Aleksandar Miladinović^4,5,6

¹Human Physiology and Sports Physiotherapy Research Group, Vrije Universiteit Brussel, Brussels, Belgium.

Frontiers in Human Neuroscience

|August 15, 2022

Summary

This summary is machine-generated.

Brain-computer interfaces (BCI) can decode lower limb movements from EEG signals with 84% accuracy in both amputees and able-bodied individuals. This demonstrates BCI feasibility for prosthetic leg control, unaffected by neuroplasticity.

Keywords:

brain-computer interface data—driven learning electroencephalography lower limb amputation machine learning neuroprosthetics

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Published on: January 13, 2022

Area of Science:

Neuroscience
Biomedical Engineering
Rehabilitation Technology

Background:

Advancements in brain-computer interfaces (BCI) enhance active prosthesis control via electroencephalogram (EEG) signal decoding.
While upper limb BCI is established, lower extremity BCI research is emerging, with knowledge gaps in neural patterns for leg movement.

Purpose of the Study:

To establish the feasibility of decoding lower limb movements using EEG data.
To investigate the impact of neuroplastic adaptations in amputees on BCI decoding performance.

Main Methods:

Collected EEG data from individuals with lower limb amputation and a matched able-bodied control group.
Trained and evaluated established BCI algorithms, previously successful for upper limb control.
Assessed decoding accuracy for discriminating lower extremity movements.

Main Results:

Achieved an average test decoding accuracy of 84% for lower limb movement discrimination using EEG data.
No significant differences in decoding performance were observed between the amputee and able-bodied groups (p = 0.99).

Conclusions:

BCI technology is feasible for controlling lower limb prosthetics.
Neuroplasticity-induced differences between amputees and able-bodied individuals do not significantly affect BCI decoding performance for lower limb movements.