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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 201–225 of 252 papers

TitleStatusHype
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
Decoding and interpreting cortical signals with a compact convolutional neural network—0
Decoding Brain Motor Imagery with various Machine Learning techniques—0
Decoding Imagined Movement in People with Multiple Sclerosis for Brain-Computer Interface Translation—0
Deep convolutional recurrent neural network for short-interval EEG motor imagery classification—0
Deep Feature Mining via Attention-based BiLSTM-GCN for Human Motor Imagery Recognition—0
Deep Learning Architecture for Motor Imaged Words—0
Deep learning for ECoG brain-computer interface: end-to-end vs. hand-crafted features—0
DGAFF: Deep Genetic Algorithm Fitness Formation for EEG Bio-Signal Channel Selection—0
Different factors determining Motor Execution and Motor Imagery performance in a serial reaction time task with intrinsic variability—0
EEG2TEXT: Open Vocabulary EEG-to-Text Decoding with EEG Pre-Training and Multi-View Transformer—0
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