Eeg Decoding
15 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
Latest papers
Transformer-based Spatial-Temporal Feature Learning for EEG Decoding
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.
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.
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 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.
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
