Eeg Decoding
8 papers with code • 0 benchmarks • 1 datasets
Benchmarks
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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
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.
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.
Uncertainty Detection in EEG Neural Decoding Models
tiehangd/ue-eeg
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In this work, we proposed an uncertainty estimation model (UE-EEG) to explore the uncertainty during the EEG decoding process, which considers both the uncertainty in the input signal and the uncertainty in the model.
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.
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.
