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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 151–175 of 252 papers

TitleStatusHype
Sub-100uW Multispectral Riemannian Classification for EEG-based Brain--Machine Interfaces—0
Subject-Independent Deep Architecture for EEG-based Motor Imagery Classification—0
Subject Specific Deep Learning Model for Motor Imagery Direction Decoding—0
Team Cogitat at NeurIPS 2021: Benchmarks for EEG Transfer Learning Competition—0
The evolution of AI approaches for motor imagery EEG-based BCIs—0
The Sandwich meta-framework for architecture agnostic deep privacy-preserving transfer learning for non-invasive brainwave decoding—0
Towards Asynchronous Motor Imagery-Based Brain-Computer Interfaces: a joint training scheme using deep learning—0
Towards Natural Brain-Machine Interaction using Endogenous Potentials based on Deep Neural Networks—0
Towards Personalized Brain-Computer Interface Application Based on Endogenous EEG Paradigms—0
Towards physiology-informed data augmentation for EEG-based BCIs—0
Transfer Learning Enhanced Common Spatial Pattern Filtering for Brain Computer Interfaces (BCIs): Overview and a New Approach—0
Transfer Learning for EEG-Based Brain-Computer Interfaces: A Review of Progress Made Since 2016—0
Transfer Learning improves MI BCI models classification accuracy in Parkinson's disease patients—0
Transfer Learning in Brain-Computer Interfaces with Adversarial Variational Autoencoders—0
Transferring BCI models from calibration to control: Observing shifts in EEG features—0
Transferring Spatial Filters via Tangent Space Alignment in Motor Imagery BCIs—0
Transferring Subspaces Between Subjects in Brain-Computer Interfacing—0
Uncertainty Quantification for Motor Imagery BCI -- Machine Learning vs. Deep Learning—0
Understanding Brain Connectivity Patterns during Motor Imagery for Brain-Computer Interfacing—0
Unsupervised Adaptive Deep Learning Method For BCI Motor Imagery Decoding—0
Unsupervised Motor Imagery Saliency Detection Based on Self-Attention Mechanism—0
Variability in Grasp Type Distinction for Myoelectric Prosthesis Control Using a Non-Invasive Brain-Machine Interface—0
Covariance Density Neural Networks—0
3D-CLMI: A Motor Imagery EEG Classification Model via Fusion of 3D-CNN and LSTM with Attention—0
A channel attention based MLP-Mixer network for motor imagery decoding with EEG—0
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