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motor imagery

GeneTech 知识引擎共收录 211 篇相关科研实体，横跨 4 个垂直领域。数据来自 OpenAlex、arXiv、Crossref、PubMed、Europe PMC 等公开学术源，全部可溯源到原文。

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代表性文献（按质量分排序，前 60 条）

• Brain-computer interface technology for motor rehabilitation in severe stroke: a narrative review.
  2026 · 仿生智能 · This review examines the application of brain-computer interface (BCI) technology for motor rehabilitation in patients with severe stroke-a population often excluded from conventional therapies due to minimal movement. B…
• EEG-based AI-BCI wheelchair advancement: Transformer-based learning with motor imagery for brain computer interface.
  2026 · 仿生智能 · This article presents an artificial intelligence integrated approach to brain-computer interface-based wheelchair development, utilizing a motor imagery right-left-hand movement mechanism for control. The system is desig…
• Motor imagery combined with brain-computer interface for stroke patients: a meta-analysis.
  2026 · 仿生智能 · Objective To systematically evaluate the effects of motor imagery combined with brain-computer interface (MI-BCI) on stroke patients. Methods Randomized controlled trials (RCTs) on MI-BCI for stroke patients were retriev…
• Yang et al. 2025 — A multi-day and high-quality EEG dataset for motor imagery brain-computer interface
  2026 · 仿生智能 · A multi-day, high-quality EEG dataset for motor imagery brain-computer interface research comprising 51 healthy subjects performing motor imagery tasks across three sessions on different days. The dataset includes two ex…
• EEG data offline and online during motor imagery for standing and sitting
  2026 · 仿生智能 · This dataset comprises electroencephalographic recordings from 32 participants performing motor imagery tasks during sit-to-stand and stand-to-sit transitions in both offline and online brain-computer interface (BCI) par…
• EEG data offline and online during motor imagery for standing and sitting
  2026 · 仿生智能 · This dataset comprises electroencephalographic recordings from 32 participants performing motor imagery tasks during sit-to-stand and stand-to-sit transitions in both offline and online brain-computer interface (BCI) par…
• BCI-FIT: A customization protocol for communication brain-computer interface systems.
  2026 · 仿生智能 · BCI-FIT is a customization protocol dataset for non-implantable communication brain-computer interface (cBCI) systems, comprising EEG recordings from five participants with speech and/or physical impairments due to amyot…
• BCI-FIT: A customization protocol for communication brain-computer interface systems.
  2026 · 仿生智能 · BCI-FIT is a customization protocol dataset for non-implantable communication brain-computer interface (cBCI) systems, comprising EEG recordings from five participants with speech and/or physical impairments due to amyot…
• Yang et al. 2025 — A multi-day and high-quality EEG dataset for motor imagery brain-computer interface
  2026 · 仿生智能 · A multi-day, high-quality EEG dataset for motor imagery brain-computer interface research comprising 51 healthy subjects performing motor imagery tasks across three sessions on different days. The dataset includes two ex…
• Exploring Imagined Movement for Brain-Computer Interface Control: An fNIRS and EEG Review.
  2025 · 仿生智能 · Brain-Computer Interfaces (BCIs) offer a non-invasive pathway for restoring motor function, particularly for individuals with limb loss. This review explored the effectiveness of Electroencephalography (EEG) and function…
• Robust Motor Imagery-Brain-Computer Interface Classification in Signal Degradation: A Multi-Window Ensemble Approach.
  2025 · 仿生智能 · Electroencephalography (EEG)-based brain–computer interface (BCI) mimics the brain’s intrinsic information-processing mechanisms by translating neural oscillations into actionable commands. In motor imagery (MI) BCI, ima…
• EEG-based brain-computer interface enables real-time robotic hand control at individual finger level
  2025 · 仿生智能 · Brain-computer interfaces (BCIs) connect human thoughts to external devices, offering the potential to enhance life quality for individuals with motor impairments and general population. Noninvasive BCIs are accessible t…
• Enhanced EEG signal classification in brain computer interfaces using hybrid deep learning models
  2025 · 仿生智能 · Brain-computer interfaces (BCIs) establish a communication pathway between the human brain and external devices by decoding neural signals. This study focuses on enhancing the classification of Motor Imagery (MI) within …
• EEG Signal Prediction for Motor Imagery Classification in Brain–Computer Interfaces
  2025 · 仿生智能 · Brain-computer interfaces (BCIs) based on motor imagery (MI) generally require EEG signals recorded from a large number of electrodes distributed across the cranial surface to achieve accurate MI classification. Not only…
• The Application of Entropy in Motor Imagery Paradigms of Brain–Computer Interfaces
  2025 · 仿生智能 · Background: In motor imagery brain–computer interface (MI-BCI) research, electroencephalogram (EEG) signals are complex and nonlinear. This complexity and nonlinearity render signal processing and classification challeng…
• Efficacy of kinesthetic motor imagery based brain computer interface combined with tDCS on upper limb function in subacute stroke
  2025 · 仿生智能 · This study investigates whether the combined effect of kinesthetic motor imagery-based brain computer interface (KI-BCI) and transcranial direct current stimulation (tDCS) on upper limb function in subacute stroke patien…
• A Synergy of Convolutional Neural Networks for Sensor-Based EEG Brain–Computer Interfaces to Enhance Motor Imagery Classification
  2025 · 仿生智能 · Enhancing motor disability assessment and its imagery classification is a significant concern in contemporary medical practice, necessitating reliable solutions to improve patient outcomes. One promising avenue is the us…
• A novel channel reduction concept to enhance the classification of motor imagery tasks in brain-computer interface systems
  2025 · 仿生智能 · Electroencephalogram (EEG) signals play a critical role in advancing brain-computer interface (BCI) systems, particularly for detecting motor imagery (MI) movements. However, analysing large volume of EEG datasets faces …
• A Novel Deep Learning Model for Motor Imagery Classification in Brain–Computer Interfaces
  2025 · 仿生智能 · Recent advancements in decoding electroencephalogram (EEG) signals for motor imagery tasks have shown significant potential. However, the intricate time–frequency dynamics and inter-channel redundancy of EEG signals rema…
• Motor imagery-based brain-computer interfaces: an exploration of multiclass motor imagery-based control for Emotiv EPOC X
  2025 · 仿生智能 · Introduction: Enhancing the command capacity of motor imagery (MI)-based brain-computer interfaces (BCIs) remains a significant challenge in neuroinformatics, especially for real-world assistive applications. This study …
• Hierarchical attention enhanced deep learning achieves high precision motor imagery classification in brain computer interfaces
  2025 · 仿生智能 · Motor imagery-based Brain-Computer Interfaces (BCIs) hold transformative potential for individuals with severe motor impairments, yet their clinical deployment remains constrained by the inherent complexity of electroenc…
• HEFMI-ICH: a hybrid EEG-fNIRS motor imagery dataset for brain-computer interface in intracerebral hemorrhage
  2025 · 仿生智能 · This study introduces the first hybrid brain-computer interface dataset specifically designed for research on intracerebral hemorrhage (ICH) rehabilitation. It offers a novel data source through the synchronized acquisit…
• Large-scale fMRI dataset for the design of motor-based Brain-Computer Interfaces
  2025 · 仿生智能 · Functional Magnetic Resonance Imaging (fMRI) data is commonly used to map sensorimotor cortical organization and to localise electrode target sites for implanted Brain-Computer Interfaces (BCIs). Functional data recorded…
• Associations between pre-cue parietal alpha oscillations and event related desynchronization in motor imagery-based brain-computer interface
  2025 · 仿生智能 · Introduction: Motor Imagery based brain-computer interfaces (MI-BCIs) offer a promising avenue for controlling external devices via neural signals generated through imagined movements. Despite their potential, the perfor…
• Effects of dual-task mode brain-computer interface based on motor imagery and virtual reality on balance and attention in patients with stroke: a randomized controlled pilot trial
  2025 · 仿生智能 · BACKGROUND: Brain-computer interface (BCI) has been shown to be beneficial in improving lower limb motility in stroke, but their effectiveness on balance and attention is unclear. In addition, current BCIs are mostly in …
• iTBS on RDLPFC improves performance of motor imagery: a brain-computer interface study combining EEG and fNIRS
  2025 · 仿生智能 · BACKGROUND: Some individuals using brain-computer interfaces (BCIs) exhibit ineffective control during motor imagery-based BCI (MI-BCI) training. MI-BCI performance correlates with the activation in the frontoparietal at…
• Hybrid brain-computer interface using error-related potential and reinforcement learning
  2025 · 仿生智能 · Brain-computer interfaces (BCIs) offer alternative communication methods for individuals with motor disabilities, aiming to improve their quality of life through external device control. However, non-invasive BCIs using …
• Cortical modulation through robotic gait training with motor imagery brain-computer interface enhances bladder function in individuals with spinal cord injury
  2025 · 仿生智能 · Neurogenic bladder (NB) dysfunction in individuals with complete spinal cord injury (SCI) is a condition that significantly affects quality of life. Despite the prevalence of interventions, there is a substantial gap in …
• Clinical Efficacy of Motor Imagery-Based Brain-Computer Interface Collaborative Training Combined with Conventional Rehabilitation Therapy on Subacute Stroke Patients with Hemiplegia
  2025 · 仿生智能 · Objective To observe the effects of motor imagery (MI)-based brain-computer interface (BCI) collaborative training on subacute stroke patients with hemiplegia. Methods A total of 40 stroke patients with hemiplegia hospit…
• When embodiment matters most: a confirmatory study on VR priming in motor imagery brain-computer interfaces training
  2025 · 仿生智能 · Background: Virtual Reality (VR) feedback is increasingly integrated into Brain-Computer Interface (BCI) applications, enhancing the Sense of Embodiment (SoE) toward virtual avatars and fostering more vivid motor imagery…
• Transfer learning with data alignment and optimal transport for EEG based motor imagery classification
  2024 · AI 安全与对齐 · Abstract Objective. The non-stationarity of electroencephalogram (EEG) signals and the variability among different subjects present significant challenges in current Brain–Computer Interfaces (BCI) research, which requir…
• A Bibliometric Review of Brain–Computer Interfaces in Motor Imagery and Steady-State Visually Evoked Potentials for Applications in Rehabilitation and Robotics
  2024 · 仿生智能 · In this paper, a bibliometric review is conducted on brain-computer interfaces (BCI) in non-invasive paradigms like motor imagery (MI) and steady-state visually evoked potentials (SSVEP) for applications in rehabilitatio…
• An EEG motor imagery dataset for brain computer interface in acute stroke patients
  2024 · 仿生智能 · The brain-computer interface (BCI) is a technology that involves direct communication with parts of the brain and has evolved rapidly in recent years; it has begun to be used in clinical practice, such as for patient reh…
• Exploring Feature Selection and Classification Techniques to Improve the Performance of an Electroencephalography-Based Motor Imagery Brain–Computer Interface System
  2024 · 仿生智能 · The accuracy of classifying motor imagery (MI) activities is a significant challenge when using brain-computer interfaces (BCIs). BCIs allow people with motor impairments to control external devices directly with their b…
• A Frequency-Domain Pattern Recognition Model for Motor Imagery-Based Brain-Computer Interface
  2024 · 仿生智能 · Brain-computer interface (BCI) is an appropriate technique for totally paralyzed people with a healthy brain. BCI based motor imagery (MI) is a common approach and widely used in neuroscience, rehabilitation engineering,…
• EEG-TCNTransformer: A Temporal Convolutional Transformer for Motor Imagery Brain–Computer Interfaces
  2024 · 仿生智能 · In brain–computer interface motor imagery (BCI-MI) systems, convolutional neural networks (CNNs) have traditionally dominated as the deep learning method of choice, demonstrating significant advancements in state-of-the-…
• On-device Learning of EEGNet-based Network For Wearable Motor Imagery Brain-Computer Interface
  2024 · 仿生智能 · Electroencephalogram (EEG)-based Brain-Computer Interfaces (BCIs) have garnered significant interest across various domains, including rehabilitation and robotics. Despite advancements in neural network-based EEG decodin…
• A shared robot control system combining augmented reality and motor imagery brain–computer interfaces with eye tracking
  2024 · 仿生智能 · Objective : Brain-computer interface (BCI) control systems monitor neural activity to detect the user's intentions, enabling device control through mental imagery. Despite their potential, decoding neural activity in rea…
• Increasing accessibility to a large brain–computer interface dataset: Curation of physionet EEG motor movement/imagery dataset for decoding and classification
  2024 · 仿生智能 · A reliable motor imagery (MI) brain-computer interface (BCI) requires accurate decoding, which in turn requires model calibration using electroencephalography (EEG) signals from subjects executing or imagining the execut…
• EEG-TCNTransformer: A Temporal Convolutional Transformer for Motor Imagery Brain-Computer Interfaces
  2024 · 仿生智能 · In Brain-Computer Interface Motor Imagery (BCI-MI) systems, Convolutional Neural Networks (CNNs) have traditionally dominated as the deep learning method of choice, demonstrating significant advancements in state-of-the-…
• Motor Imagery Teleoperation of a Mobile Robot Using a Low-Cost Brain-Computer Interface for Multi-Day Validation
  2024 · 仿生智能 · Brain-computer interfaces (BCI) have the potential to provide transformative control in prosthetics, assistive technologies (wheelchairs), robotics, and human-computer interfaces. While Motor Imagery (MI) offers an intui…
• A Gait Imagery-Based Brain-Computer Interface with Visual feedback for Spinal Cord Injury Rehabilitation on Lokomat
  2024 · 仿生智能 · Abstract: Objective: Motor Imagery (MI)-based Brain-ComputerInterfaces (BCIs) have been proposed for the rehabilitation ofpeople with disabilities, being a big challenge their successfulapplication to restore motor funct…
• Explainable Deep-Learning Prediction for Brain–Computer Interfaces Supported Lower Extremity Motor Gains Based on Multistate Fusion
  2024 · 仿生智能 · Predicting the potential for recovery of motor function in stroke patients who undergo specific rehabilitation treatments is an important and major challenge. Recently, electroencephalography (EEG) has shown potential in…
• Enhancing Real-Time Cursor Control with Motor Imagery and Deep Neural Networks for Brain–Computer Interfaces
  2024 · 仿生智能 · This paper advances real-time cursor control for individuals with motor impairments through a novel brain–computer interface (BCI) system based solely on motor imagery. We introduce an enhanced deep neural network (DNN) …
• A motor imagery classification model based on hybrid brain-computer interface and multitask learning of electroencephalographic and electromyographic deep features
  2024 · 仿生智能 · Objective: Extracting deep features from participants' bioelectric signals and constructing models are key research directions in motor imagery (MI) classification tasks. In this study, we constructed a multimodal multit…
• Developing a tablet-based brain-computer interface and robotic prototype for upper limb rehabilitation
  2024 · 仿生智能 · Background The current study explores the integration of a motor imagery (MI)-based BCI system with robotic rehabilitation designed for upper limb function recovery in stroke patients. Methods We developed a tablet deplo…
• CTNet: a convolutional transformer network for EEG-based motor imagery classification
  2024 · 仿生智能 · Brain-computer interface (BCI) technology bridges the direct communication between the brain and machines, unlocking new possibilities for human interaction and rehabilitation. EEG-based motor imagery (MI) plays a pivota…
• A Spatial-Spectral and Temporal Dual Prototype Network for Motor Imagery Brain-Computer Interface
  2024 · 仿生智能 · Motor imagery electroencephalogram (MI-EEG) decoding plays a crucial role in developing motor imagery brain-computer interfaces (MI-BCIs). However, decoding intentions from MI remains challenging due to the inherent comp…
• Characterization of Event Related Desynchronization in Chronic Stroke Using Motor Imagery Based Brain Computer Interface for Upper Limb Rehabilitation
  2024 · 仿生智能 · OBJECTIVE: Motor imagery-based brain-computer interface (MI-BCI) is a promising novel mode of stroke rehabilitation. The current study aims to investigate the feasibility of MI-BCI in upper limb rehabilitation of chronic…
• Unraveling motor imagery brain patterns using explainable artificial intelligence based on Shapley values
  2024 · 仿生智能 · BACKGROUND AND OBJECTIVE: Motor imagery (MI) based brain-computer interfaces (BCIs) are widely used in rehabilitation due to the close relationship that exists between MI and motor execution (ME). However, the underlying…
• A Comprehensive Review on Brain–Computer Interface (BCI)-Based Machine and Deep Learning Algorithms for Stroke Rehabilitation
  2024 · 仿生智能 · This literature review explores the pivotal role of brain–computer interface (BCI) technology, coupled with electroencephalogram (EEG) technology, in advancing rehabilitation for individuals with damaged muscles and moto…
• Gamification of motor imagery brain-computer interface training protocols: A systematic review
  2024 · 仿生智能 · Current Motor Imagery Brain-Computer Interfaces (MI-BCI) require a lengthy and monotonous training procedure to train both the system and the user. Considering many users struggle with effective control of MI-BCI systems…
• Machine learning techniques for electroencephalogram based brain-computer interface: A systematic literature review
  2023 · 仿生智能 · Brain-computer interface systems with Electroencephalogram (EEG), especially those use motor-imagery (MI) signals, have demonstrated the ability to control electromechanical devices with promising results. EEG being easy…
• A Review of Online Classification Performance in Motor Imagery-Based Brain–Computer Interfaces for Stroke Neurorehabilitation
  2023 · 仿生智能 · Motor imagery (MI)-based brain–computer interfaces (BCI) have shown increased potential for the rehabilitation of stroke patients; nonetheless, their implementation in clinical practice has been restricted due to their l…
• Review of public motor imagery and execution datasets in brain-computer interfaces
  2023 · 仿生智能 · The demand for public datasets has increased as data-driven methodologies have been introduced in the field of brain-computer interfaces (BCIs). Indeed, many BCI datasets are available in various platforms or repositorie…
• Bayesian Machine Learning for Motor-Imagery EEG: Lesson Notes
  2026 · 仿生智能 · Graduate-level lesson notes on the machine learning behind motor-imagery electroencephalogram (MI-EEG) brain-computer interface (BCI) classification. Eleven self-contained sections move from the signal, through the class…
• Bayesian Machine Learning for Motor-Imagery EEG: Lesson Notes
  2026 · 仿生智能 · Graduate-level lesson notes on the machine learning behind motor-imagery electroencephalogram (MI-EEG) brain-computer interface (BCI) classification. Eleven self-contained sections move from the signal, through the class…
• Diagnosing Motor-Imagery BCI Failure: A Three-Step Diagnostic Framework
  2026 · 仿生智能 · Version 3.1.0. This version adds the four figures. The v3.0.0 PDF carried the captions but not the artwork, because the manuscript is prepared with figures supplied as separate files, which is what the journal requires a…
• Diagnosing Motor-Imagery BCI Failure: A Three-Step Diagnostic Framework
  2026 · 仿生智能 · Version 3.0.0. This version corresponds to the manuscript submitted to the Journal of Neuroscience Methods on 2 August 2026. It supersedes v2.0.0, which described a two-step framework evaluated on a single cohort. The fr…
• Feature Engineering: Lesson Materials
  2026 · 仿生智能 · Graduate-level lesson materials on feature engineering and feature selection for machine learning, taught through motor-imagery electroencephalogram (MI-EEG) classification as a running case study. The material assumes a…

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