Skip to content

BCI project using Deep Learning to decode EEG signals.

Notifications You must be signed in to change notification settings

amr-farahat/BCI-P300

Repository files navigation

P300 Brain Computer Interface (BCI)

Brain Computer Interface project in which I use wide and deep convolutional neural networks to decode P300 component in EEG signals for a speller application. I also use saliency maps for visualizing the features learned by the model. These maps are then quantified to reveal the task-related brain dynamics.


Related Publications

Abstract and Poster published in Bernstein conference for computational neuroscience in Berlin in Septemper 2018.

Paper Convolutional Neural Networks for Decoding of Covert Attention Focus and Saliency Maps for EEG Feature Visualization.

For Citations.

@Article{Farahat_2019, Title = {Convolutional neural networks for decoding of covert attention focus and saliency maps for {EEG} feature visualization}, Author = {Amr Farahat and Christoph Reichert and Catherine M Sweeney-Reed and Hermann Hinrichs}, Journal = {Journal of Neural Engineering}, Year = {2019}, Month = {oct}, Number = {6}, Pages = {066010}, Volume = {16}, Doi = {10.1088/1741-2552/ab3bb4}, Publisher = {{IOP} Publishing}, Url = {https://doi.org/10.1088%2F1741-2552%2Fab3bb4} }

Preprint

About

BCI project using Deep Learning to decode EEG signals.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published