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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
MI-BMInet: An Efficient Convolutional Neural Network for Motor Imagery Brain--Machine Interfaces with EEG Channel Selection—0
Minimizing subject-dependent calibration for BCI with Riemannian transfer learning—0
Model-Agnostic Meta-Learning for EEG Motor Imagery Decoding in Brain-Computer-Interfacing—0
Modeling Grasp Motor Imagery through Deep Conditional Generative Models—0
Motor-Imagery-Based Brain Computer Interface using Signal Derivation and Aggregation Functions—0
Motor Imagery Classification based on CNN-GRU Network with Spatio-Temporal Feature Representation—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
A Computationally Efficient Multiclass Time-Frequency Common Spatial Pattern Analysis on EEG Motor Imagery—0
A Convolutional Spiking Network for Gesture Recognition in Brain-Computer Interfaces—0
A Deep Learning Based Ternary Task Classification System Using Gramian Angular Summation Field in fNIRS Neuroimaging Data—0
Adversarial Stimuli: Attacking Brain-Computer Interfaces via Perturbed Sensory Events—0
A Dynamic Domain Adaptation Deep Learning Network for EEG-based Motor Imagery Classification—0
AFPM: Alignment-based Frame Patch Modeling for Cross-Dataset EEG Decoding—0
A Generative System for Robot-to-Human Handovers: from Intent Inference to Spatial Configuration Imagery—0
A Human-Machine Joint Learning Framework to Boost Endogenous BCI Training—0
A Hybrid Brain-Computer Interface Using Motor Imagery and SSVEP Based on Convolutional Neural Network—0
A Many Objective Optimization Approach for Transfer Learning in EEG Classification—0
An Accurate EEGNet-based Motor-Imagery Brain-Computer Interface for Low-Power Edge Computing—0
Adaptive Subspace Sampling for Class Imbalance Processing-Some clarifications, algorithm, and further investigation including applications to Brain Computer Interface—0
A New Method for Features Normalization in Motor Imagery Few-Shot Learning using Resting-State—0
An Improved EEG Acquisition Protocol Facilitates Localized Neural Activation—0
An intertwined neural network model for EEG classification in brain-computer interfaces—0
A Parametric Time Frequency-Conditional Granger Causality Method Using Ultra-regularized Orthogonal Least Squares and Multiwavelets for Dynamic Connectivity Analysis in EEGs—0
A self-paced BCI system with low latency for motor imagery onset detection based on time series prediction paradigm—0
A SPA-based Manifold Learning Framework for Motor Imagery EEG Data Classification—0
A Study on Stroke Rehabilitation through Task-Oriented Control of a Haptic Device via Near-Infrared Spectroscopy-Based BCI—0
A Subject-Independent Brain-Computer Interface Framework Based on Supervised Autoencoder—0
A Technique Based on Chaos for Brain Computer Interfacing—0
A Ternary Bi-Directional LSTM Classification for Brain Activation Pattern Recognition Using fNIRS—0
BDAN: Mitigating Temporal Difference Across Electrodes in Cross-Subject Motor Imagery Classification via Generative Bridging Domain—0
Binarization Methods for Motor-Imagery Brain-Computer Interface Classification—0
Boosted Convolutional Neural Networks for Motor Imagery EEG Decoding with Multiwavelet-based Time-Frequency Conditional Granger Causality Analysis—0
Brain-Computer Interface with Corrupted EEG Data: A Tensor Completion Approach—0
Can EEG resting state data benefit data-driven approaches for motor-imagery decoding?—0
CATVis: Context-Aware Thought Visualization—0
Channel Reflection: Knowledge-Driven Data Augmentation for EEG-Based Brain-Computer Interfaces—0
Classification of EEG Motor Imagery Using Deep Learning for Brain-Computer Interface Systems—0
Classification of eye-state using EEG recordings: speed-up gains using signal epochs and mutual information measure—0
Classification of Upper Arm Movements from EEG signals using Machine Learning with ICA Analysis—0
Classifying Single-Trial EEG during Motor Imagery with a Small Training Set—0
CLEAN-MI: A Scalable and Efficient Pipeline for Constructing High-Quality Neurodata in Motor Imagery Paradigm—0
Common Spatial Generative Adversarial Networks based EEG Data Augmentation for Cross-Subject Brain-Computer Interface—0
Community Detection from Multiple Observations: from Product Graph Model to Brain Applications—0
Covariate Shift Estimation based Adaptive Ensemble Learning for Handling Non-Stationarity in Motor Imagery related EEG-based Brain-Computer Interface—0
CropCat: Data Augmentation for Smoothing the Feature Distribution of EEG Signals—0
Cross-Correlation Based Discriminant Criterion for Channel Selection in Motor Imagery BCI Systems—0
Dataset Refinement for Improving the Generalization Ability of the EEG Decoding Model—0
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