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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 201–225 of 252 papers

TitleStatusHype
Novel EEG-based BCIs for Elderly Rehabilitation Enhancement—0
On-device Learning of EEGNet-based Network For Wearable Motor Imagery Brain-Computer Interface—0
On the use of generative deep neural networks to synthesize artificial multichannel EEG signals—0
Optimized Feature Selection and Neural Network-Based Classification of Motor Imagery Using EEG Signals—0
Partial Least Square Regression via Three-factor SVD-type Manifold Optimization for EEG Decoding—0
Performance of Dual-Augmented Lagrangian Method and Common Spatial Patterns applied in classification of Motor-Imagery BCI—0
Phase/amplitude synchronization of brain signals during motor imagery BCI tasks—0
Phase Synchrony Component Self-Organization in Brain Computer Interface—0
Preliminary Assessment of hands motor imagery in theta- and beta-bands for Brain-Machine-Interfaces using functional connectivity analysis—0
Probabilistic Learning Vector Quantization on Manifold of Symmetric Positive Definite Matrices—0
Pseudo Channel: Time Embedding for Motor Imagery Decoding—0
Pseudo-online framework for BCI evaluation: A MOABB perspective—0
Real-Time EEG Classification via Coresets for BCI Applications—0
Research on Event-Related Desynchronization of Motor Imagery and Movement Based on Localized EEG Cortical Sources—0
Riemannian Flow Matching for Brain Connectivity Matrices via Pullback Geometry—0
RLEEGNet: Integrating Brain-Computer Interfaces with Adaptive AI for Intuitive Responsiveness and High-Accuracy Motor Imagery Classification—0
Robust Feature Engineering Techniques for Designing Efficient Motor Imagery-Based BCI-Systems—0
Robust Spatial Filtering with Beta Divergence—0
Scatter-based common spatial patterns -- a unified spatial filtering framework—0
SCDM: Unified Representation Learning for EEG-to-fNIRS Cross-Modal Generation in MI-BCIs—0
Siamese Neural Networks for EEG-based Brain-computer Interfaces—0
Sinc-based convolutional neural networks for EEG-BCI-based motor imagery classification—0
Single Channel-based Motor Imagery Classification using Fisher's Ratio and Pearson Correlation—0
Motor Imagery Classification for Asynchronous EEG-Based Brain-Computer InterfacesCode0
Feature interpretability in BCIs: exploring the role of network lateralizationCode0
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