Eeg Decoding
18 papers with code • 1 benchmarks • 2 datasets
EEG Decoding - extracting useful information directly from EEG data.
Benchmarks
These leaderboards are used to track progress in Eeg Decoding
| Trend | Dataset | Best Model | Paper | Code | Compare |
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CWL EEG/fMRI Dataset
NMED-T
Most implemented papers
Deep learning with convolutional neural networks for EEG decoding and visualization
PLEASE READ AND CITE THE REVISED VERSION at Human Brain Mapping: http://onlinelibrary. wiley. com/doi/10. 1002/hbm. 23730/full Code available here: https://github. com/robintibor/braindecode
Transformer-based Spatial-Temporal Feature Learning for EEG Decoding
anranknight/EEG-Transformer
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As far as we know, it is the first time that a detailed and complete method based on the transformer idea has been proposed in this field.
Physics-inform attention temporal convolutional network for EEG-based motor imagery classification
Altaheri/EEG-ATCNet
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IEEE Transactions on Industrial Informatics 2022
In this paper, we propose an attention-based temporal convolutional network (ATCNet) for EEG-based motor imagery classification.
Time-space-frequency feature Fusion for 3-channel motor imagery classification
TSFF-Net comprises four main components: time-frequency representation, time-frequency feature extraction, time-space feature extraction, and feature fusion and classification.
Decoding P300 Variability using Convolutional Neural Networks
vlawhern/arl-eegmodels
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Frontiers in Human Neuroscience 2019
Deep convolutional neural networks (CNN) have previously been shown to be useful tools for signal decoding and analysis in a variety of complex domains, such as image processing and speech recognition.
Subject-Aware Contrastive Learning for Biosignals
zacharycbrown/ssl_baselines_for_biosignal_feature_extraction
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Datasets for biosignals, such as electroencephalogram (EEG) and electrocardiogram (ECG), often have noisy labels and have limited number of subjects (<100).
Decoding kinetic features of hand motor preparation from single‐trial EEG using convolutional neural networks
ragatti/STSnet
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These results show that movement speed and force can be accurately predicted from single‐trial EEG, and that the prediction strategies may provide useful neurophysiological information about motor preparation.
Uncertainty Detection and Reduction in Neural Decoding of EEG Signals
tiehangd/ue-eeg
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In this work, we proposed an uncertainty estimation and reduction model (UNCER) to quantify and mitigate the uncertainty during the EEG decoding process.
ExBrainable: An Open-Source GUI for CNN-based EEG Decoding and Model Interpretation
We have developed a graphic user interface (GUI), ExBrainable, dedicated to convolutional neural networks (CNN) model training and visualization in electroencephalography (EEG) decoding.
MAtt: A Manifold Attention Network for EEG Decoding
cecnl/matt
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Recognition of electroencephalographic (EEG) signals highly affect the efficiency of non-invasive brain-computer interfaces (BCIs).
