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Motor Imagery
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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
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
CATVis: Context-Aware Thought Visualization
Uncertainty Quantification for Motor Imagery BCI -- Machine Learning vs. Deep Learning
DBConformer: Dual-Branch Convolutional Transformer for EEG Decoding
AGTCNet: A Graph-Temporal Approach for Principled Motor Imagery EEG Classification
CLEAN-MI: A Scalable and Efficient Pipeline for Constructing High-Quality Neurodata in Motor Imagery Paradigm
Dataset combining EEG, eye-tracking, and high-speed video for ocular activity analysis across BCI paradigms
EEG Foundation Models for BCI Learn Diverse Features of Electrophysiology
Riemannian Flow Matching for Brain Connectivity Matrices via Pullback Geometry
Covariance Density Neural Networks
Transferring Spatial Filters via Tangent Space Alignment in Motor Imagery BCIs
SSTAF: Spatial-Spectral-Temporal Attention Fusion Transformer for Motor Imagery Classification
Optimized Feature Selection and Neural Network-Based Classification of Motor Imagery Using EEG Signals
A Generative System for Robot-to-Human Handovers: from Intent Inference to Spatial Configuration Imagery
Research on Event-Related Desynchronization of Motor Imagery and Movement Based on Localized EEG Cortical Sources
Error-related Potential driven Reinforcement Learning for adaptive Brain-Computer Interfaces
MVCNet: Multi-View Contrastive Network for Motor Imagery Classification
Minima Possible Weights: A Homogenous Deep Ensemble Method for Cross-Subject Motor Imagery Classification
The Case for Cleaner Biosignals: High-fidelity Neural Compressor Enables Transfer from Cleaner iEEG to Noisier EEG
Fine-Tuning Strategies for Continual Online EEG Motor Imagery Decoding: Insights from a Large-Scale Longitudinal Study
Subject Specific Deep Learning Model for Motor Imagery Direction Decoding
Human-AI Teaming Using Large Language Models: Boosting Brain-Computer Interfacing (BCI) and Brain Research
Low count of optically pumped magnetometers furnishes a reliable real-time access to sensorimotor rhythm
Motor Imagery Classification for Asynchronous EEG-Based Brain-Computer Interfaces
Robust Feature Engineering Techniques for Designing Efficient Motor Imagery-Based BCI-Systems
T-TIME: Test-Time Information Maximization Ensemble for Plug-and-Play BCIs
EEG-Based Mental Imagery Task Adaptation via Ensemble of Weight-Decomposed Low-Rank Adapters
Channel Reflection: Knowledge-Driven Data Augmentation for EEG-Based Brain-Computer Interfaces
Effect of Simulated Space Conditions on functional Connectivity
Different factors determining Motor Execution and Motor Imagery performance in a serial reaction time task with intrinsic variability
Federated Motor Imagery Classification for Privacy-Preserving Brain-Computer Interfaces
Decoding Imagined Movement in People with Multiple Sclerosis for Brain-Computer Interface Translation
The more, the better? Evaluating the role of EEG preprocessing for deep learning applications
Towards Personalized Brain-Computer Interface Application Based on Endogenous EEG Paradigms
EEG-DCNet: A Fast and Accurate MI-EEG Dilated CNN Classification Method
Graph-Dictionary Signal Model for Sparse Representations of Multivariate Data
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