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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 1–25 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
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