ArticlePloS one2017
Upper limb movements can be decoded from the time-domain of low-frequency EEG.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
58 citing papers in PubMed, 249 citations in OpenAlex.
- Efficacy of kinesthetic motor imagery based brain computer interface combined with tDCS on upper limb function in subacute stroke.Scientific reports · 2025Trial
- Motor imagery training of goal-directed reaching in relation to imagery of reaching and grasping in healthy people.Scientific reports · 2022Trial
- Exploring representations of human grasping in neural, muscle and kinematic signals.Scientific reports · 2018Trial
- EEG patterns of self-paced movement imaginations towards externally-cued and internally-selected targets.Scientific reports · 2018Trial
- Exploring Kinematics Information Decoding from EEG Slow Cortical Potentials During Movement Imagination and Observation.Sensors (Basel, Switzerland) · 2026Article
- Decoding motor imagery related to major mimetic muscles from electroencephalography.Journal of neuroengineering and rehabilitation · 2026Article
- Adaptive Neural Reorganization Enables Real-Time Finger-Level Robotic Control in BCI-Naïve Stroke Survivors.bioRxiv : the preprint server for biology · 2026Article
- Spatially Masked Regression Reveals Local and Distributed Predictability in Electrophysiological Recordings.ArXiv · 2026Article
- A large electroencephalogram database of freewill reaching and grasping tasks for brain machine interfaces.Scientific data · 2025Article
- Exploring Imagined Movement for Brain-Computer Interface Control: An fNIRS and EEG Review.Brain sciences · 2025Review
- Altered Sensorimotor Cortical Activities During Forearm Movement in Humans With Spinal Cord Injury.CNS neuroscience & therapeutics · 2025Article
- EEG-based brain-computer interface enables real-time robotic hand control at individual finger level.Nature communications · 2025Article
- A multi-modal dataset of electroencephalography and functional near-infrared spectroscopy recordings for motor imagery of multi-types of joints from unilateral upper limb.Scientific data · 2025Article
- Assessing Motor Cortical Activity: How Repetitions Impact Motor Execution and Imagery Analysis.Psychophysiology · 2025Article
- Closed-loop rehabilitation of upper-limb dyskinesia after stroke: from natural motion to neuronal microfluidics.Journal of neuroengineering and rehabilitation · 2025Review
- Non-Invasive Brain-Computer Interfaces: State of the Art and Trends.IEEE reviews in biomedical engineering · 2025Review
- Decoding the brain-machine interaction for upper limb assistive technologies: advances and challenges.Frontiers in human neuroscience · 2025Review
- Unraveling EEG correlates of unimanual finger movements: insights from non-repetitive flexion and extension tasks.Journal of neuroengineering and rehabilitation · 2024Article
- Dynamical Embedding of Single-Channel Electroencephalogram for Artifact Subspace Reconstruction.Sensors (Basel, Switzerland) · 2024Article
- MOVING: A Multi-Modal Dataset of EEG Signals and Virtual Glove Hand Tracking.Sensors (Basel, Switzerland) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
How neural correlates of movements are represented in the human brain is of ongoing interest and has been researched with invasive and non-invasive methods. In this study, we analyzed the encoding of single upper limb movements in the time-domain of low-frequency electroencephalography (EEG) signals. Fifteen healthy subjects executed and imagined six different sustained upper limb movements. We classified these six movements and a rest class and obtained significant average classification accuracies of 55% (movement vs movement) and 87% (movement vs rest) for executed movements, and 27% and 73%, respectively, for imagined movements. Furthermore, we analyzed the classifier patterns in the source space and located the brain areas conveying discriminative movement information. The classifier patterns indicate that mainly premotor areas, primary motor cortex, somatosensory cortex and posterior parietal cortex convey discriminative movement information. The decoding of single upper limb movements is specially interesting in the context of a more natural non-invasive control of e.g., a motor neuroprosthesis or a robotic arm in highly motor disabled persons.
Indexed as
Identifiers
What OpenQuestion holds
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.