ArticleFrontiers in computational neuroscience2018
Estimation of Neuromuscular Primitives from EEG Slow Cortical Potentials in Incomplete Spinal Cord Injury Individuals for a New Class of Brain-Machine Interfaces.
Article in Frontiers in computational neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 22 citations in OpenAlex.
- Combined Use of EMG and EEG Techniques for Neuromotor Assessment in Rehabilitative Applications: A Systematic Review.Sensors (Basel, Switzerland) · 2021Pooled it
- User preference in the personalized control of an ankle prosthesis: a case study.Journal of neuroengineering and rehabilitation · 2026Article
- A muscle synergy-based method to improve robot-assisted movements.Scientific reports · 2025Article
- Myoelectric Control in Rehabilitative and Assistive Soft Exoskeletons: A Comprehensive Review of Trends, Challenges, and Integration with Soft Robotic Devices.Biomimetics (Basel, Switzerland) · 2025Review
- Time-frequency-space transformer EEG decoding for spinal cord injury.Cognitive neurodynamics · 2024Article
- Decoding temporal muscle synergy patterns based on brain activity for upper extremity in ADL movements.Cognitive neurodynamics · 2024Article
- EMPT: a sparsity Transformer for EEG-based motor imagery recognition.Frontiers in neuroscience · 2024Article
- Human-in-the-loop optimization of wearable device parameters using an EMG-based objective function.Wearable technologies · 2024Article
- Voluntary control of wearable robotic exoskeletons by patients with paresis via neuromechanical modeling.Journal of neuroengineering and rehabilitation · 2019Article
- Neuromusculoskeletal Modeling-Based Prostheses for Recovery After Spinal Cord Injury.Frontiers in neurorobotics · 2019Review
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
One of the current challenges in human motor rehabilitation is the robust application of Brain-Machine Interfaces to assistive technologies such as powered lower limb exoskeletons. Reliable decoding of motor intentions and accurate timing of the robotic device actuation is fundamental to optimally enhance the patient's functional improvement. Several studies show that it may be possible to extract motor intentions from electroencephalographic (EEG) signals. These findings, although notable, suggests that current techniques are still far from being systematically applied to an accurate real-time control of rehabilitation or assistive devices. Here we propose the estimation of spinal primitives of multi-muscle control from EEG, using electromyography (EMG) dimensionality reduction as a solution to increase the robustness of the method. We successfully apply this methodology, both to healthy and incomplete spinal cord injury (SCI) patients, to identify muscle contraction during periodical knee extension from the EEG. We then introduce a novel performance metric, which accurately evaluates muscle primitive activations.
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