Motor Imagery
58 papers with code • 0 benchmarks • 0 datasets
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
Benchmarks
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Libraries
Use these libraries to find Motor Imagery models and implementations
Most implemented papers
Spatio-Temporal EEG Representation Learning on Riemannian Manifold and Euclidean Space
guangyizhangbci/eeg_riemannian
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Moreover, our proposed method learns the temporal information via differential entropy and logarithm power spectrum density features extracted from EEG signals in a Euclidean space using a deep long short-term memory network with a soft attention mechanism.
Interval-valued aggregation functions based on moderate deviations applied to Motor-Imagery-Based Brain Computer Interface
Fuminides/athena
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In this work we study the use of moderate deviation functions to measure similarity and dissimilarity among a set of given interval-valued data.
Comparison of Classification Algorithms Towards Subject-Specific and Subject-Independent BCI
Our results show that classification algorithms for SS models display large variance in performance.
Towards Real-World BCI: CCSPNet, A Compact Subject-Independent Motor Imagery Framework
A conventional brain-computer interface (BCI) requires a complete data gathering, training, and calibration phase for each user before it can be used.
Motor-Imagery-Based Brain Computer Interface using Signal Derivation and Aggregation Functions
Fuminides/athena
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In BCI applications, the ElectroEncephaloGraphy is a very popular measurement for brain dynamics because of its non-invasive nature.
MIN2Net: End-to-End Multi-Task Learning for Subject-Independent Motor Imagery EEG Classification
We integrate deep metric learning into a multi-task autoencoder to learn a compact and discriminative latent representation from EEG and perform classification simultaneously.
RIGOLETTO -- RIemannian GeOmetry LEarning: applicaTion To cOnnectivity. A contribution to the Clinical BCI Challenge -- WCCI2020
This short technical report describes the approach submitted to the Clinical BCI Challenge-WCCI2020.
Mixed-Precision Quantization and Parallel Implementation of Multispectral Riemannian Classification for Brain--Machine Interfaces
With Motor-Imagery (MI) Brain--Machine Interfaces (BMIs) we may control machines by merely thinking of performing a motor action.
FBCNet: A Multi-view Convolutional Neural Network for Brain-Computer Interface
With this design, we compare FBCNet with state-of-the-art (SOTA) BCI algorithm on four MI datasets: The BCI competition IV dataset 2a (BCIC-IV-2a), the OpenBMI dataset, and two large datasets from chronic stroke patients.
Classification of Motor Imagery EEG Signals by Using a Divergence Based Convolutional Neural Network
In this study, we have investigated the effect of the augmentation process on the classification performance of MI EEG signals instead of using a preceding transformation such as the CSP, and we have demonstrated that by resulting in high success rates for the classification of MI EEGs, the augmentation process is able to compete with the CSP.
