Trial reportScientific reports2018
Exploring representations of human grasping in neural, muscle and kinematic signals.
Trial report in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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.
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Who cites it
18 citing papers in PubMed, 43 citations in OpenAlex.
- A large electroencephalogram database of freewill reaching and grasping tasks for brain machine interfaces.Scientific data · 2025Article
- 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
- MOVING: A Multi-Modal Dataset of EEG Signals and Virtual Glove Hand Tracking.Sensors (Basel, Switzerland) · 2024Article
- Towards unlocking motor control in spinal cord injured by applying an online EEG-based framework to decode motor intention, trajectory and error processing.Scientific reports · 2024Article
- Understanding the capacity of children with congenital unilateral below-elbow deficiency to actuate their affected muscles.Scientific reports · 2024Article
- Electromyography and kinematics data of the hand in activities of daily living with special interest for ergonomics.Scientific data · 2023Article
- Decoding executed and imagined grasping movements from distributed non-motor brain areas using a Riemannian decoder.Frontiers in neuroscience · 2023Article
- Feel Your Reach: An EEG-Based Framework to Continuously Detect Goal-Directed Movements and Error Processing to Gate Kinesthetic Feedback Informed Artificial Arm Control.Frontiers in human neuroscience · 2022Review
- Workshops of the Eighth International Brain-Computer Interface Meeting: BCIs: The Next Frontier.Brain computer interfaces (Abingdon, England) · 2022Article
- Inference of Upcoming Human Grasp Using EMG During Reach-to-Grasp Movement.Frontiers in neuroscience · 2022Article
- Disentangling human grasping type from the object's intrinsic properties using low-frequency EEG signals.Neuroimage. Reports · 2021Article
- Multimodal treatment for spinal cord injury: a sword of neuroregeneration upon neuromodulation.Neural regeneration research · 2020Review
- Neuroregeneration and plasticity: a review of the physiological mechanisms for achieving functional recovery postinjury.Military Medical Research · 2020Review
- A calibrated database of kinematics and EMG of the forearm and hand during activities of daily living.Scientific data · 2019Article
- Contributions of the Left and the Right Hemispheres on Language-Induced Grip Force Modulation of the Left Hand in Unimanual Tasks.Medicina (Kaunas, Lithuania) · 2019Article
- Workshops of the Seventh International Brain-Computer Interface Meeting: Not Getting Lost in Translation.Brain computer interfaces (Abingdon, England) · 2019Article
- The Neural Representation of Force across Grasp Types in Motor Cortex of Humans with Tetraplegia.eNeuroArticle
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Movement covariates, such as electromyographic or kinematic activity, have been proposed as candidates for the neural representation of hand control. However, it remains unclear how these movement covariates are reflected in electroencephalographic (EEG) activity during different stages of grasping movements. In this exploratory study, we simultaneously acquired EEG, kinematic and electromyographic recordings of human subjects performing 33 types of grasps, yielding the largest such dataset to date. We observed that EEG activity reflected different movement covariates in different stages of grasping. During the pre-shaping stage, centro-parietal EEG in the lower beta frequency band reflected the object's shape and size, whereas during the finalization and holding stages, contralateral parietal EEG in the mu frequency band reflected muscle activity. These findings contribute to the understanding of the temporal organization of neural grasping patterns, and could inform the design of noninvasive neuroprosthetics and brain-computer interfaces with more natural control.
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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.