ArticleFrontiers in neurorobotics2020
Analysis of the EEG Rhythms Based on the Empirical Mode Decomposition During Motor Imagery When Using a Lower-Limb Exoskeleton. A Case Study.
Article in Frontiers in neurorobotics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04995367 (BCI-Based Control for Ankle Exoskeleton T-FLEX), which is not on this map. Cited by 17 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
BCI-Based Control for Ankle Exoskeleton T-FLEX: Comparison of Visual and Haptic Feedback With Stroke Survivors
Who cites it
17 citing papers in PubMed.
- EEG-Controlled Exoskeleton for Walking and Standing: A Longitudinal Multimodal Dataset of Healthy Individuals.Scientific data · 2026Article
- Human-machine Interface using functional electrostimulation and inertial sensors for lower limb rehabilitation in spinal cord injury individuals: a proof of concept.Medical & biological engineering & computing · 2026Article
- Leveraging Cross-Subject Transfer Learning and Signal Augmentation for Enhanced RGB Color Decoding from EEG Data.Brain sciences · 2026Article
- Obstacle Feature Information-Based Motion Decision-Making Method for Obstacle-Crossing Motions in Lower Limb Exoskeleton Robots.Biomimetics (Basel, Switzerland) · 2025Article
- Time-Frequency Analysis of Motor Imagery During Plantar and Dorsal Flexion Movements Using a Low-Cost Ankle Exoskeleton.Sensors (Basel, Switzerland) · 2025Article
- Augmenting Circadian Biology Research With Data Science.Journal of biological rhythms · 2025Review
- An EEG database for the cognitive assessment of motor imagery during walking with a lower-limb exoskeleton.Scientific data · 2023Article
- IMF-Based MF and HS Energy Feature Information of F5, and F6 Movement and Motor Imagery EEG Signals in Delta Rhythms Using HHT.Sensors (Basel, Switzerland) · 2023Article
- Identification of Lower-Limb Motor Tasks via Brain-Computer Interfaces: A Topical Overview.Sensors (Basel, Switzerland) · 2022Review
- Sensors and Actuation Technologies in Exoskeletons: A Review.Sensors (Basel, Switzerland) · 2022Review
- Decoding EEG rhythms offline and online during motor imagery for standing and sitting based on a brain-computer interface.Frontiers in neuroinformatics · 2022Article
- DEEMD-SPP: A Novel Framework for Emotion Recognition Based on EEG Signals.Frontiers in psychiatry · 2022Article
- An sEMG-Based Human-Exoskeleton Interface Fusing Convolutional Neural Networks With Hand-Crafted Features.Frontiers in neurorobotics · 2022Article
- BCI-Based Control for Ankle Exoskeleton T-FLEX: Comparison of Visual and Haptic Stimuli with Stroke Survivors.Sensors (Basel, Switzerland) · 2021Article
- A Comprehensive Review on Critical Issues and Possible Solutions of Motor Imagery Based Electroencephalography Brain-Computer Interface.Sensors (Basel, Switzerland) · 2021Review
- MCSNet: Channel Synergy-Based Human-Exoskeleton Interface With Surface Electromyogram.Frontiers in neuroscience · 2021Article
- Exoskeleton Active Walking Assistance Control Framework Based on Frequency Adaptive Dynamics Movement Primitives.Frontiers in neurorobotics · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The use of brain-machine interfaces in combination with robotic exoskeletons is usually based on the analysis of the changes in power that some brain rhythms experience during a motion event. However, this variation in power is frequently obtained through frequency filtering and power estimation using the Fourier analysis. This paper explores the decomposition of the brain rhythms based on the Empirical Mode Decomposition, as an alternative for the analysis of electroencephalographic (EEG) signals, due to its adaptive capability to the local oscillations of the data, showcasing it as a viable tool for future BMI algorithms based on motor related events.
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Registered trials
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.