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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 101–150 of 252 papers

TitleStatusHype
GET: A Generative EEG Transformer for Continuous Context-Based Neural Signals—0
Motor Imagery Task Alters Dynamics of Human Body Posture—0
Pseudo Channel: Time Embedding for Motor Imagery Decoding—0
NeuroAssist: Enhancing Cognitive-Computer Synergy with Adaptive AI and Advanced Neural Decoding for Efficient EEG Signal Classification—0
EEG2TEXT: Open Vocabulary EEG-to-Text Decoding with EEG Pre-Training and Multi-View Transformer—0
Quantifying Spatial Domain Explanations in BCI using Earth Mover's DistanceCode0
EEG_RL-Net: Enhancing EEG MI Classification through Reinforcement Learning-Optimised Graph Neural Networks—0
EEGEncoder: Advancing BCI with Transformer-Based Motor Imagery Classification—0
EEG_GLT-Net: Optimising EEG Graphs for Real-time Motor Imagery Signals Classification—0
BDAN: Mitigating Temporal Difference Across Electrodes in Cross-Subject Motor Imagery Classification via Generative Bridging Domain—0
The Sandwich meta-framework for architecture agnostic deep privacy-preserving transfer learning for non-invasive brainwave decoding—0
Unsupervised Adaptive Deep Learning Method For BCI Motor Imagery Decoding—0
Transferring BCI models from calibration to control: Observing shifts in EEG features—0
Uncertainty Quantification for cross-subject Motor Imagery classificationCode0
Spatiotemporal Pooling on Appropriate Topological Maps Represented as Two-Dimensional Images for EEG Classification—0
FingerNet: EEG Decoding of A Fine Motor Imagery with Finger-tapping Task Based on A Deep Neural Network—0
Multiview Contrastive Learning for Unsupervised Domain Adaptation in Brain–Computer Interfaces—0
RLEEGNet: Integrating Brain-Computer Interfaces with Adaptive AI for Intuitive Responsiveness and High-Accuracy Motor Imagery Classification—0
Subject-Independent Deep Architecture for EEG-based Motor Imagery Classification—0
3D-CLMI: A Motor Imagery EEG Classification Model via Fusion of 3D-CNN and LSTM with Attention—0
Sparse Multitask Learning for Efficient Neural Representation of Motor Imagery and Execution—0
NiSNN-A: Non-iterative Spiking Neural Networks with Attention with Application to Motor Imagery EEG Classification—0
Latent Alignment with Deep Set EEG Decoders—0
Applying Dimensionality Reduction as Precursor to LSTM-CNN Models for Classifying Imagery and Motor Signals in ECoG-Based BCIsCode0
EEG-DG: A Multi-Source Domain Generalization Framework for Motor Imagery EEG ClassificationCode0
Improved Motor Imagery Classification Using Adaptive Spatial Filters Based on Particle Swarm Optimization Algorithm—0
Enhancing Motor Imagery Decoding in Brain Computer Interfaces using Riemann Tangent Space Mapping and Cross Frequency Coupling—0
LGL-BCI: A Lightweight Geometric Learning Framework for Motor Imagery-Based Brain-Computer Interfaces—0
Graph Neural Network-based EEG Classification: A Survey—0
Intentional binding enhances hybrid BCI control—0
Phase Synchrony Component Self-Organization in Brain Computer Interface—0
A Dynamic Domain Adaptation Deep Learning Network for EEG-based Motor Imagery Classification—0
Transfer Learning between Motor Imagery Datasets using Deep Learning -- Validation of Framework and Comparison of DatasetsCode0
A Human-Machine Joint Learning Framework to Boost Endogenous BCI Training—0
Pseudo-online framework for BCI evaluation: A MOABB perspective—0
Aggregating Intrinsic Information to Enhance BCI Performance through Federated LearningCode0
Deep Learning Architecture for Motor Imaged Words—0
Feature Reweighting for EEG-based Motor Imagery Classification—0
End-to-End Deep Transfer Learning for Calibration-free Motor Imagery Brain Computer Interfaces—0
EEG Decoding for Datasets with Heterogenous Electrode Configurations using Transfer Learning Graph Neural Networks—0
Decoding Brain Motor Imagery with various Machine Learning techniques—0
A Convolutional Spiking Network for Gesture Recognition in Brain-Computer Interfaces—0
Scatter-based common spatial patterns -- a unified spatial filtering framework—0
Classification of BCI-EEG based on augmented covariance matrixCode0
CropCat: Data Augmentation for Smoothing the Feature Distribution of EEG Signals—0
A Hybrid Brain-Computer Interface Using Motor Imagery and SSVEP Based on Convolutional Neural Network—0
Adversarial Stimuli: Attacking Brain-Computer Interfaces via Perturbed Sensory Events—0
Motor imagery classification using EEG spectrograms—0
Spatial Graph Signal Interpolation with an Application for Merging BCI Datasets with Various Dimensionalities—0
The evolution of AI approaches for motor imagery EEG-based BCIs—0
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