Trial reportJournal of neuroengineering and rehabilitation2018
On the design of EEG-based movement decoders for completely paralyzed stroke patients.
Trial report in Journal of neuroengineering and rehabilitation, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Application of Brain-Computer Interface in Upper Limb Dysfunction After Stroke: A Systematic Review and Meta-Analysis of Randomized Controlled Trials.Frontiers in human neuroscience · 2022Pooled it
- Efficacy of kinesthetic motor imagery based brain computer interface combined with tDCS on upper limb function in subacute stroke.Scientific reports · 2025Trial
- The Acute Effects of Motor Imagery Combined With Action Observation Breathing Exercise on Cardiorespiratory Responses, Brain Activity, and Cognition: A Randomized, Controlled Trial.Cardiovascular therapeutics · 2025Trial
- Cortical beta coherence provides a stronger non-invasive predictor of movement vigor than local beta power.Journal of neuroengineering and rehabilitation · 2026Article
- Decoding multi-class motor attempt from the affected unilateral limbs in chronic stroke patients.Journal of neuroengineering and rehabilitation · 2026Article
- Uncovering attempted movements of the paralyzed upper limb after stroke through EEG and EMG.Journal of neuroengineering and rehabilitation · 2025Article
- A novel channel reduction concept to enhance the classification of motor imagery tasks in brain-computer interface systems.PloS one · 2025Article
- Brain-computer interface in critical care and rehabilitation.Acute and critical care · 2024Article
- Challenges of neural interfaces for stroke motor rehabilitation.Frontiers in human neuroscience · 2023Article
- Reconstructing Synergy-Based Hand Grasp Kinematics from Electroencephalographic Signals.Sensors (Basel, Switzerland) · 2022Article
- Multimodal Human-Exoskeleton Interface for Lower Limb Movement Prediction Through a Dense Co-Attention Symmetric Mechanism.Frontiers in neuroscience · 2022Article
- The Differences Between Motor Attempt and Motor Imagery in Brain-Computer Interface Accuracy and Event-Related Desynchronization of Patients With Hemiplegia.Frontiers in neurorobotics · 2021Article
- Comparison of Smoothing Filters in Analysis of EEG Data for the Medical Diagnostics Purposes.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundBrain machine interface (BMI) technology has demonstrated its efficacy for rehabilitation of paralyzed chronic stroke patients. The critical component in BMI-training consists of the associative connection (contingency) between the intention and the feedback provided. However, the relationship between the BMI design and its performance in stroke patients is still an open question.
methodsIn this study we compare different methodologies to design a BMI for rehabilitation and evaluate their effects on movement intention decoding performance. We analyze the data of 37 chronic stroke patients who underwent 4 weeks of BMI intervention with different types of association between their brain activity and the proprioceptive feedback. We simulate the pseudo-online performance that a BMI would have under different conditions, varying: (1) the cortical source of activity (i.e., ipsilesional, contralesional, bihemispheric), (2) the type of spatial filter applied, (3) the EEG frequency band, (4) the type of classifier; and also evaluated the use of residual EMG activity to decode the movement intentions.
resultsWe observed a significant influence of the different BMI designs on the obtained performances. Our results revealed that using bihemispheric beta activity with a common average reference and an adaptive support vector machine led to the best classification results. Furthermore, the decoding results based on brain activity were significantly higher than those based on muscle activity.
conclusionsThis paper underscores the relevance of the different parameters used to decode movement, using EEG in severely paralyzed stroke patients. We demonstrated significant differences in performance for the different designs, which supports further research that should elucidate if those approaches leading to higher accuracies also induce higher motor recovery in paralyzed stroke patients.
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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.