ArticleFrontiers in human neuroscience2022
A data-driven machine learning approach for brain-computer interfaces targeting lower limb neuroprosthetics.
Article in Frontiers in human neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 10 citations in OpenAlex.
- Virtual Reality (VR) Paradigm-Agnostic Motor Imagery Decoding Using Lightweight Network With Adaptive Attention Mechanism.Journal of medical systems · 2025Article
- Toward Natural Limb Function: A New Era in Prosthetic Innovation.Annals of neurology · 2025Review
- User Evaluation of a Shared Robot Control System Combining BCI and Eye Tracking in a Portable Augmented Reality User Interface.Sensors (Basel, Switzerland) · 2024Article
- Cortical signals analysis to recognize intralimb mobility using modified RNN and various EEG quantities.Heliyon · 2024Article
- Electroencephalogram-based adaptive closed-loop brain-computer interface in neurorehabilitation: a review.Frontiers in computational neuroscience · 2024Review
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Authors and funding
8 authors at 5 institutions in 7 countries.
Funding
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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 address this, we collected data from multiple individuals with lower limb amputation and a matched able-bodied control group. Using these data, we trained and evaluated common BCI methods that have already been proven effective for upper limb BCI. With an average test decoding accuracy of 84% for both groups, our results show that it is possible to discriminate different lower extremity movements using EEG data with good accuracy. There are no significant differences (
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.