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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 126–150 of 252 papers

TitleStatusHype
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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