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
Exploring Embedding Methods in Binary Hyperdimensional Computing: A Case Study for Motor-Imagery based Brain-Computer Interfaces
All these methods, differing in complexity, aim to represent EEG signals in binary HD space, e. g. with 10, 000 bits.
Different Set Domain Adaptation for Brain-Computer Interfaces: A Label Alignment Approach
Currently, most domain adaptation approaches require the source domains to have the same feature space and label space as the target domain, which limits their applications, as the auxiliary data may have different feature spaces and/or different label spaces.
Classification of High-Dimensional Motor Imagery Tasks based on An End-to-end role assigned convolutional neural network
ByeonghooLee-ku/ICASSP2020-2020-code
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A brain-computer interface (BCI) provides a direct communication pathway between user and external devices.
Q-EEGNet: an Energy-Efficient 8-bit Quantized Parallel EEGNet Implementation for Edge Motor-Imagery Brain--Machine Interfaces
We quantize weights and activations to 8-bit fixed-point with a negligible accuracy loss of 0. 4% on 4-class MI, and present an energy-efficient hardware-aware implementation on the Mr. Wolf parallel ultra-low power (PULP) System-on-Chip (SoC) by utilizing its custom RISC-V ISA extensions and 8-core compute cluster.
Federated Transfer Learning for EEG Signal Classification
DashanGao/Federated-Transfer-Learning-for-EEG
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The success of deep learning (DL) methods in the Brain-Computer Interfaces (BCI) field for classification of electroencephalographic (EEG) recordings has been restricted by the lack of large datasets.
EEG-TCNet: An Accurate Temporal Convolutional Network for Embedded Motor-Imagery Brain-Machine Interfaces
Experimental results on the BCI Competition IV-2a dataset show that EEG-TCNet achieves 77. 35% classification accuracy in 4-class MI.
GCNs-Net: A Graph Convolutional Neural Network Approach for Decoding Time-resolved EEG Motor Imagery Signals
To conclude, the GCNs-Net filters EEG signals based on the functional topological relationship, which manages to decode relevant features for brain motor imagery.
Attention-based Graph ResNet for Motor Intent Detection from Raw EEG signals
In previous studies, decoding electroencephalography (EEG) signals has not considered the topological relationship of EEG electrodes.
Transfer Learning for Motor Imagery Based Brain-Computer Interfaces: A Complete Pipeline
Transfer learning (TL) has been widely used in motor imagery (MI) based brain-computer interfaces (BCIs) to reduce the calibration effort for a new subject, and demonstrated promising performance.
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.
