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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 151–175 of 252 papers

TitleStatusHype
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
Source Data Selection for Brain-Computer Interfaces based on Simple Features—0
Sparse Multitask Learning for Efficient Neural Representation of Motor Imagery and Execution—0
Spatial Filtering for Brain Computer Interfaces: A Comparison between the Common Spatial Pattern and Its Variant—0
Spatial Graph Signal Interpolation with an Application for Merging BCI Datasets with Various Dimensionalities—0
Spatial-Spectral Boosting Analysis for Stroke Patients' Motor Imagery EEG in Rehabilitation Training—0
Spatiotemporal Pooling on Appropriate Topological Maps Represented as Two-Dimensional Images for EEG Classification—0
Spectrally Adaptive Common Spatial Patterns—0
SSTAF: Spatial-Spectral-Temporal Attention Fusion Transformer for Motor Imagery Classification—0
Sub-100uW Multispectral Riemannian Classification for EEG-based Brain--Machine Interfaces—0
Subject-Independent Deep Architecture for EEG-based Motor Imagery Classification—0
Subject Specific Deep Learning Model for Motor Imagery Direction Decoding—0
Team Cogitat at NeurIPS 2021: Benchmarks for EEG Transfer Learning Competition—0
The evolution of AI approaches for motor imagery EEG-based BCIs—0
The Sandwich meta-framework for architecture agnostic deep privacy-preserving transfer learning for non-invasive brainwave decoding—0
Towards Asynchronous Motor Imagery-Based Brain-Computer Interfaces: a joint training scheme using deep learning—0
Towards Natural Brain-Machine Interaction using Endogenous Potentials based on Deep Neural Networks—0
Towards Personalized Brain-Computer Interface Application Based on Endogenous EEG Paradigms—0
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