ArticleIEEE transactions on bio-medical engineering2016
EEG Source Imaging Enhances the Decoding of Complex Right-Hand Motor Imagery Tasks.
Article in IEEE transactions on bio-medical engineering, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 97 papers.
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Who cites it
97 citing papers in PubMed, 370 citations in OpenAlex.
- The reorganization mechanism of brain functional networks by long term actual exercise and exercise imagination training: spectrum and network topology analysis based on EEG.Journal of neuroengineering and rehabilitation · 2026Trial
- PG-MCTFormer: A Prior-Guided Multi-Scale Convolutional Transformer for Interpretable Motor Imagery EEG Classification.Biomimetics (Basel, Switzerland) · 2026Article
- A geometry aware framework enhances noninvasive mapping of whole human brain dynamics.Nature biomedical engineering · 2026Article
- Decoding multi-class motor attempt from the affected unilateral limbs in chronic stroke patients.Journal of neuroengineering and rehabilitation · 2026Article
- Bimanual attempts improving EEG-based hand movement decoding in stroke patients with hemiplegia.Frontiers in human neuroscience · 2026Article
- Human brain imaging with high-density electroencephalography: Techniques and applications.The Journal of physiology · 2026Review
- Decoding visual object recognition from EEG signals.PloS one · 2026Article
- Brain-computer interface control with artificial intelligence copilots.Nature machine intelligence · 2025Article
- Review
- Enhancing classification of a large lower-limb motor imagery EEG dataset for BCI in knee pain patients.Scientific data · 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
- Single-Trial Electroencephalography Discrimination of Real, Regulated, Isometric Wrist Extension and Wrist Flexion.Biomimetics (Basel, Switzerland) · 2025Article
- Method for EEG signal recognition based on multi-domain feature fusion and optimization of multi-kernel extreme learning machine.Scientific reports · 2025Article
- Exploring individual biases in BCI research and users: Does gender matter?Frontiers in human neuroscience · 2025Article
- Long-Term Brain-Computer Interface Functional Electrical Stimulation Enhances Neuroplasticity and Functional Recovery in Elderly Stroke: A 4.5-Year Longitudinal Study Integrating Electroencephalography Biomarkers and Clinical Assessments.Research (Washington, D.C.) · 2025Article
- Non-Invasive Brain-Computer Interfaces: State of the Art and Trends.IEEE reviews in biomedical engineering · 2025Review
- A bimodal deep learning network based on CNN for fine motor imagery.Cognitive neurodynamics · 2024Article
- Partial prior transfer learning based on self-attention CNN for EEG decoding in stroke patients.Scientific reports · 2024Article
- Motor imagery decoding using source optimized transfer learning based on multi-loss fusion CNN.Cognitive neurodynamics · 2024Article
37 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
goalSensorimotor-based brain-computer interfaces (BCIs) have achieved successful control of real and virtual devices in up to three dimensions; however, the traditional sensor-based paradigm limits the intuitive use of these systems. Many control signals for state-of-the-art BCIs involve imagining the movement of body parts that have little to do with the output command, revealing a cognitive disconnection between the user's intent and the action of the end effector. Therefore, there is a need to develop techniques that can identify with high spatial resolution the self-modulated neural activity reflective of the actions of a helpful output device.
methodsWe extend previous EEG source imaging (ESI) work to decoding natural hand/wrist manipulations by applying a novel technique to classifying four complex motor imaginations of the right hand: flexion, extension, supination, and pronation.
resultsWe report an increase of up to 18.6% for individual task classification and 12.7% for overall classification using the proposed ESI approach over the traditional sensor-based method.
conclusionESI is able to enhance BCI performance of decoding complex right-hand motor imagery tasks. SIGNIFICANCE: This study may lead to the development of BCI systems with naturalistic and intuitive motor imaginations, thus facilitating broad use of noninvasive BCIs.
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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.