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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 26–50 of 252 papers

TitleStatusHype
Human-AI Teaming Using Large Language Models: Boosting Brain-Computer Interfacing (BCI) and Brain Research—0
Low count of optically pumped magnetometers furnishes a reliable real-time access to sensorimotor rhythm—0
Motor Imagery Classification for Asynchronous EEG-Based Brain-Computer InterfacesCode0
Robust Feature Engineering Techniques for Designing Efficient Motor Imagery-Based BCI-Systems—0
T-TIME: Test-Time Information Maximization Ensemble for Plug-and-Play BCIsCode1
EEG-Based Mental Imagery Task Adaptation via Ensemble of Weight-Decomposed Low-Rank Adapters—0
Channel Reflection: Knowledge-Driven Data Augmentation for EEG-Based Brain-Computer Interfaces—0
Effect of Simulated Space Conditions on functional Connectivity—0
Different factors determining Motor Execution and Motor Imagery performance in a serial reaction time task with intrinsic variability—0
Federated Motor Imagery Classification for Privacy-Preserving Brain-Computer InterfacesCode0
Decoding Imagined Movement in People with Multiple Sclerosis for Brain-Computer Interface Translation—0
The more, the better? Evaluating the role of EEG preprocessing for deep learning applicationsCode1
Towards Personalized Brain-Computer Interface Application Based on Endogenous EEG Paradigms—0
EEG-DCNet: A Fast and Accurate MI-EEG Dilated CNN Classification MethodCode0
Graph-Dictionary Signal Model for Sparse Representations of Multivariate Data—0
From Electrode to Global Brain: Integrating Multi- and Cross-Scale Brain Connections and Interactions Under Cross-Subject and Within-Subject Scenarios—0
Alignment-Based Adversarial Training (ABAT) for Improving the Robustness and Accuracy of EEG-Based BCIsCode0
Dataset Refinement for Improving the Generalization Ability of the EEG Decoding Model—0
Feature Selection via Dynamic Graph-based Attention Block in MI-based EEG Signals—0
Neurophysiological Analysis in Motor and Sensory Cortices for Improving Motor Imagination—0
Can EEG resting state data benefit data-driven approaches for motor-imagery decoding?—0
Source Data Selection for Brain-Computer Interfaces based on Simple Features—0
Variability in Grasp Type Distinction for Myoelectric Prosthesis Control Using a Non-Invasive Brain-Machine Interface—0
MixNet: Joining Force of Classical and Modern Approaches Toward the Comprehensive Pipeline in Motor Imagery EEG ClassificationCode0
CTNet: A Convolutional Transformer Network for EEG-Based Motor Imagery ClassificationCode3
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