ArticlePloS one2015
EEG classification of different imaginary movements within the same limb.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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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
30 citing papers in PubMed, 1 synthesis or guideline pooled it, 134 citations in OpenAlex.
- The Effectiveness of Intermittent Theta Burst Stimulation for Stroke Patients With Upper Limb Impairments: A Systematic Review and Meta-Analysis.Frontiers in neurology · 2022Pooled it
- Target-oriented motor imagery for grasping action: different characteristics of brain activation between kinesthetic and visual imagery.Scientific reports · 2019Trial
- A multi-paradigm and longitudinal EEG dataset including the "sixth-finger" and "affected-hand" motor imagery of stroke patients.Scientific data · 2026Article
- Classification of Different Motor Imagery Tasks with the Same Limb Using Electroencephalographic Signals.Sensors (Basel, Switzerland) · 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
- Decoding the brain-machine interaction for upper limb assistive technologies: advances and challenges.Frontiers in human neuroscience · 2025Review
- A Bibliometric Review of Brain-Computer Interfaces in Motor Imagery and Steady-State Visually Evoked Potentials for Applications in Rehabilitation and Robotics.Sensors (Basel, Switzerland) · 2024Review
- Exploring Machine Learning Classification of Movement Phases in Hemiparetic Stroke Patients: A Controlled EEG-tDCS Study.Brain sciences · 2024Article
- Noninvasive brain stimulation during EEG improves machine learning classiResearch square · 2024Article
- Classification of bruxism based on time-frequency and nonlinear features of single channel EEG.BMC oral health · 2024Article
- Decoding reach-to-grasp from EEG using classifiers trained with data from the contralateral limb.Frontiers in human neuroscience · 2023Article
- Recognition of motor intentions from EEGs of the same upper limb by signal traceability and Riemannian geometry features.Frontiers in neuroscience · 2023Article
- Predicting motor behavior: an efficient EEG signal processing pipeline to detect brain states with potential therapeutic relevance for VR-based neurorehabilitation.Virtual reality · 2023Article
- Metric Learning in Freewill EEG Pre-Movement and Movement Intention Classification for Brain Machine Interfaces.Frontiers in human neuroscience · 2022Article
- Multibranch convolutional neural network with contrastive representation learning for decoding same limb motor imagery tasks.Frontiers in human neuroscience · 2022Article
- Functional Near-Infrared Spectroscopy for the Classification of Motor-Related Brain Activity on the Sensor-Level.Sensors (Basel, Switzerland) · 2020Article
- Mu-Beta event-related (de)synchronization and EEG classification of left-right foot dorsiflexion kinaesthetic motor imagery for BCI.PloS one · 2020Article
- Classification of Motor Functions from Electroencephalogram (EEG) Signals Based on an Integrated Method Comprised of Common Spatial Pattern and Wavelet Transform Framework.Sensors (Basel, Switzerland) · 2019Article
- Human motor decoding from neural signals: a review.BMC biomedical engineering · 2019Review
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
The task of discriminating the motor imagery of different movements within the same limb using electroencephalography (EEG) signals is challenging because these imaginary movements have close spatial representations on the motor cortex area. There is, however, a pressing need to succeed in this task. The reason is that the ability to classify different same-limb imaginary movements could increase the number of control dimensions of a brain-computer interface (BCI). In this paper, we propose a 3-class BCI system that discriminates EEG signals corresponding to rest, imaginary grasp movements, and imaginary elbow movements. Besides, the differences between simple motor imagery and goal-oriented motor imagery in terms of their topographical distributions and classification accuracies are also being investigated. To the best of our knowledge, both problems have not been explored in the literature. Based on the EEG data recorded from 12 able-bodied individuals, we have demonstrated that same-limb motor imagery classification is possible. For the binary classification of imaginary grasp and elbow (goal-oriented) movements, the average accuracy achieved is 66.9%. For the 3-class problem of discriminating rest against imaginary grasp and elbow movements, the average classification accuracy achieved is 60.7%, which is greater than the random classification accuracy of 33.3%. Our results also show that goal-oriented imaginary elbow movements lead to a better classification performance compared to simple imaginary elbow movements. This proposed BCI system could potentially be used in controlling a robotic rehabilitation system, which can assist stroke patients in performing task-specific exercises.
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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.