14 papers with code • 1 benchmarks • 2 datasets
EEG Decoding - extracting useful information directly from EEG data.
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
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.
In this paper, we propose an attention-based temporal convolutional network (ATCNet) for EEG-based 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.
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.
Decoding kinetic features of hand motor preparation from single‐trial EEG using convolutional neural networks
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.
In this work, we proposed an uncertainty estimation and reduction model (UNCER) to quantify and mitigate the uncertainty during the EEG decoding process.
We have developed a graphic user interface (GUI), ExBrainable, dedicated to convolutional neural networks (CNN) model training and visualization in electroencephalography (EEG) decoding.