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Motor Imagery

Classification of examples recorded under the Motor Imagery paradigm, as part of Brain-Computer Interfaces (BCI).

A number of motor imagery datasets can be downloaded using the MOABB library: motor imagery datasets list

Papers

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Showing 1–50 of 252 papers

TitleStatusHype
NeuroXAI: Adaptive, robust, explainable surrogate framework for determination of channel importance in EEG applicationCode1
AFPM: Alignment-based Frame Patch Modeling for Cross-Dataset EEG Decoding—0
CATVis: Context-Aware Thought Visualization—0
Uncertainty Quantification for Motor Imagery BCI -- Machine Learning vs. Deep Learning—0
AGTCNet: A Graph-Temporal Approach for Principled Motor Imagery EEG ClassificationCode1
DBConformer: Dual-Branch Convolutional Transformer for EEG DecodingCode2
TCANet: A Temporal Convolutional Attention Network for Motor Imagery EEG DecodingCode1
CLEAN-MI: A Scalable and Efficient Pipeline for Constructing High-Quality Neurodata in Motor Imagery Paradigm—0
Dataset combining EEG, eye-tracking, and high-speed video for ocular activity analysis across BCI paradigmsCode0
EEG Foundation Models for BCI Learn Diverse Features of Electrophysiology—0
Riemannian Flow Matching for Brain Connectivity Matrices via Pullback Geometry—0
Covariance Density Neural Networks—0
Transferring Spatial Filters via Tangent Space Alignment in Motor Imagery BCIs—0
SSTAF: Spatial-Spectral-Temporal Attention Fusion Transformer for Motor Imagery Classification—0
Multi-scale convolutional transformer network for motor imagery brain-computer interfaceCode2
Optimized Feature Selection and Neural Network-Based Classification of Motor Imagery Using EEG Signals—0
A Generative System for Robot-to-Human Handovers: from Intent Inference to Spatial Configuration Imagery—0
Research on Event-Related Desynchronization of Motor Imagery and Movement Based on Localized EEG Cortical Sources—0
Motor Imagery EEG Signals: Multi-Task Classification and Subject Identification with a Lightweight CNN—0
Error-related Potential driven Reinforcement Learning for adaptive Brain-Computer Interfaces—0
Minima Possible Weights: A Homogenous Deep Ensemble Method for Cross-Subject Motor Imagery ClassificationCode0
MVCNet: Multi-View Contrastive Network for Motor Imagery ClassificationCode1
The Case for Cleaner Biosignals: High-fidelity Neural Compressor Enables Transfer from Cleaner iEEG to Noisier EEGCode1
Fine-Tuning Strategies for Continual Online EEG Motor Imagery Decoding: Insights from a Large-Scale Longitudinal StudyCode1
Subject Specific Deep Learning Model for Motor Imagery Direction Decoding—0
Human-AI Teaming Using Large Language Models: Boosting Brain-Computer Interfacing (BCI) and Brain Research—0
Low count of optically pumped magnetometers furnishes a reliable real-time access to sensorimotor rhythm—0
Motor Imagery Classification for Asynchronous EEG-Based Brain-Computer InterfacesCode0
Robust Feature Engineering Techniques for Designing Efficient Motor Imagery-Based BCI-Systems—0
T-TIME: Test-Time Information Maximization Ensemble for Plug-and-Play BCIsCode1
EEG-Based Mental Imagery Task Adaptation via Ensemble of Weight-Decomposed Low-Rank Adapters—0
Channel Reflection: Knowledge-Driven Data Augmentation for EEG-Based Brain-Computer Interfaces—0
Effect of Simulated Space Conditions on functional Connectivity—0
Different factors determining Motor Execution and Motor Imagery performance in a serial reaction time task with intrinsic variability—0
Federated Motor Imagery Classification for Privacy-Preserving Brain-Computer InterfacesCode0
Decoding Imagined Movement in People with Multiple Sclerosis for Brain-Computer Interface Translation—0
The more, the better? Evaluating the role of EEG preprocessing for deep learning applicationsCode1
Towards Personalized Brain-Computer Interface Application Based on Endogenous EEG Paradigms—0
EEG-DCNet: A Fast and Accurate MI-EEG Dilated CNN Classification MethodCode0
Graph-Dictionary Signal Model for Sparse Representations of Multivariate Data—0
From Electrode to Global Brain: Integrating Multi- and Cross-Scale Brain Connections and Interactions Under Cross-Subject and Within-Subject Scenarios—0
Alignment-Based Adversarial Training (ABAT) for Improving the Robustness and Accuracy of EEG-Based BCIsCode0
Dataset Refinement for Improving the Generalization Ability of the EEG Decoding Model—0
Feature Selection via Dynamic Graph-based Attention Block in MI-based EEG Signals—0
Neurophysiological Analysis in Motor and Sensory Cortices for Improving Motor Imagination—0
Can EEG resting state data benefit data-driven approaches for motor-imagery decoding?—0
Source Data Selection for Brain-Computer Interfaces based on Simple Features—0
Variability in Grasp Type Distinction for Myoelectric Prosthesis Control Using a Non-Invasive Brain-Machine Interface—0
MixNet: Joining Force of Classical and Modern Approaches Toward the Comprehensive Pipeline in Motor Imagery EEG ClassificationCode0
CTNet: A Convolutional Transformer Network for EEG-Based Motor Imagery ClassificationCode3
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