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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
A 1D CNN for high accuracy classification and transfer learning in motor imagery EEG-based brain-computer interfaceCode1
Neural Network-Based Feature Extraction for Multi-Class Motor Imagery Classification—0
Uncertainty Detection and Reduction in Neural Decoding of EEG SignalsCode0
Sub-100uW Multispectral Riemannian Classification for EEG-based Brain--Machine Interfaces—0
Interpretable Convolutional Neural Networks for Subject-Independent Motor Imagery Classification—0
Minimizing subject-dependent calibration for BCI with Riemannian transfer learning—0
Functional connectivity ensemble method to enhance BCI performance (FUCONE)Code1
A channel attention based MLP-Mixer network for motor imagery decoding with EEG—0
Novel EEG-based BCIs for Elderly Rehabilitation Enhancement—0
Deep convolutional recurrent neural network for short-interval EEG motor imagery classification—0
MutualGraphNet: A novel model for motor imagery classification—0
A SPA-based Manifold Learning Framework for Motor Imagery EEG Data Classification—0
Classification of Upper Arm Movements from EEG signals using Machine Learning with ICA Analysis—0
Motor Imagery Classification based on CNN-GRU Network with Spatio-Temporal Feature Representation—0
Complex common spatial patterns on time-frequency decomposed EEG for brain-computer interfaceCode0
Towards Natural Brain-Machine Interaction using Endogenous Potentials based on Deep Neural Networks—0
Classification of Hand-Grasp Movements of Stroke Patients using EEG DataCode1
Generating Ten BCI Commands Using Four Simple Motor Imageries—0
CNN-based Approaches For Cross-Subject Classification in Motor Imagery: From The State-of-The-Art to DynamicNetCode1
A magnetoencephalography dataset for motor and cognitive imagery-based brain-computer interfaceCode1
Classification of Motor Imagery EEG Signals by Using a Divergence Based Convolutional Neural NetworkCode0
FBCNet: A Multi-view Convolutional Neural Network for Brain-Computer InterfaceCode1
A New Method for Features Normalization in Motor Imagery Few-Shot Learning using Resting-State—0
Model-Agnostic Meta-Learning for EEG Motor Imagery Decoding in Brain-Computer-Interfacing—0
Inter-subject Deep Transfer Learning for Motor Imagery EEG Decoding—0
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