Eeg Decoding

14 papers with code • 1 benchmarks • 2 datasets

EEG Decoding - extracting useful information directly from EEG data.

Most implemented papers

Deep learning with convolutional neural networks for EEG decoding and visualization

braindecode/braindecode 15 Mar 2017

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 11 Jun 2021

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 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

miaozhengqing/lmda-code 4 Apr 2023

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 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 30 Jun 2020

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 11 Aug 2020

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 28 Dec 2021

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

CECNL/ExBrainable 10 Jan 2022

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 5 Oct 2022

Recognition of electroencephalographic (EEG) signals highly affect the efficiency of non-invasive brain-computer interfaces (BCIs).