ArticlePloS one2015
Decoding Sensorimotor Rhythms during Robotic-Assisted Treadmill Walking for Brain Computer Interface (BCI) Applications.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 32 papers, 5 of them syntheses that pooled it.
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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
32 citing papers in PubMed, 5 syntheses or guidelines pooled it, 68 citations in OpenAlex.
- Systematic review of AI/ML applications in multi-domain robotic rehabilitation: trends, gaps, and future directions.Journal of neuroengineering and rehabilitation · 2025Pooled it
- Control strategies used in lower limb exoskeletons for gait rehabilitation after brain injury: a systematic review and analysis of clinical effectiveness.Journal of neuroengineering and rehabilitation · 2023Pooled it
- Brain activity during real-time walking and with walking interventions after stroke: a systematic review.Journal of neuroengineering and rehabilitation · 2021Pooled it
- A Systematic Review Establishing the Current State-of-the-Art, the Limitations, and the DESIRED Checklist in Studies of Direct Neural Interfacing With Robotic Gait Devices in Stroke Rehabilitation.Frontiers in neuroscience · 2020Pooled it
- EEG-Based Control for Upper and Lower Limb Exoskeletons and Prostheses: A Systematic Review.Sensors (Basel, Switzerland) · 2018Pooled it
- The classification of walking and phases of gait using EEG: a scoping review.Journal of neuroengineering and rehabilitation · 2026Article
- EEG-Controlled Exoskeleton for Walking and Standing: A Longitudinal Multimodal Dataset of Healthy Individuals.Scientific data · 2026Article
- Deep Learning Discrimination for BCI Implementation Using 3D Convolutional Neural Network and EEG Topographic Maps.Advances in experimental medicine and biology · 2026Article
- Community perspectives regarding brain-computer interfaces: A cross-sectional study of community-dwelling adults in the UK.PLOS digital health · 2025Article
- Neuro-motor controlled wearable augmentations: current research and emerging trends.Frontiers in neurorobotics · 2024Review
- Altered cortical activation patterns in post-stroke patients during walking with two-channel functional electrical stimulation: a functional near-infrared spectroscopy observational study.Frontiers in neurology · 2024Article
- A global bibliometric and visualized analysis of gait analysis and artificial intelligence research from 1992 to 2022.Frontiers in robotics and AI · 2023Article
- Stroke survivor perceptions of using an exoskeleton during acute gait rehabilitation.Scientific reports · 2022Article
- Relation between Cortical Activation and Effort during Robot-Mediated Walking in Healthy People: A Functional Near-Infrared Spectroscopy Neuroimaging Study (fNIRS).Sensors (Basel, Switzerland) · 2022Article
- Phase-dependent Brain Activation of the Frontal and Parietal Regions During Walking After Stroke - An fNIRS Study.Frontiers in neurology · 2022Article
- Involvement of the Rostromedial Prefrontal Cortex in Human-Robot Interaction: fNIRS Evidence From a Robot-Assisted Motor Task.Frontiers in neurorobotics · 2022Article
- Classifying Motion Intention of Step Length and Synchronous Walking Speed by Functional Near-Infrared Spectroscopy.Cyborg and bionic systems (Washington, D.C.) · 2021Article
- A Roadmap Towards Standards for Neurally Controlled End Effectors.IEEE open journal of engineering in medicine and biology · 2021Article
- Assessment of an Assistive Control Approach Applied in an Active Knee Orthosis Plus Walker for Post-Stroke Gait Rehabilitation.Sensors (Basel, Switzerland) · 2020Article
- Overground Walking Decreases Alpha Activity and Entrains Eye Movements in Humans.Frontiers in human neuroscience · 2020Article
Corrections and comments
- Erratum issued
Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Locomotor malfunction represents a major problem in some neurological disorders like stroke and spinal cord injury. Robot-assisted walking devices have been used during rehabilitation of patients with these ailments for regaining and improving walking ability. Previous studies showed the advantage of brain-computer interface (BCI) based robot-assisted training combined with physical therapy in the rehabilitation of the upper limb after stroke. Therefore, stroke patients with walking disorders might also benefit from using BCI robot-assisted training protocols. In order to develop such BCI, it is necessary to evaluate the feasibility to decode walking intention from cortical patterns during robot-assisted gait training. Spectral patterns in the electroencephalogram (EEG) related to robot-assisted active and passive walking were investigated in 10 healthy volunteers (mean age 32.3±10.8, six female) and in three acute stroke patients (all male, mean age 46.7±16.9, Berg Balance Scale 20±12.8). A logistic regression classifier was used to distinguish walking from baseline in these spectral EEG patterns. Mean classification accuracies of 94.0±5.4% and 93.1±7.9%, respectively, were reached when active and passive walking were compared against baseline. The classification performance between passive and active walking was 83.4±7.4%. A classification accuracy of 89.9±5.7% was achieved in the stroke patients when comparing walking and baseline. Furthermore, in the healthy volunteers modulation of low gamma activity in central midline areas was found to be associated with the gait cycle phases, but not in the stroke patients. Our results demonstrate the feasibility of BCI-based robotic-assisted training devices for gait rehabilitation.
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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.