EEG-DL

Why we included this project

EEG-DL bundles TensorFlow implementations of the deep learning models most often used for EEG classification, from CNNs and recurrent networks to graph convolutional and transformer encoders. For researchers working on motor-imagery decoding, sleep staging, emotion recognition, or brain-computer interfaces more broadly, that breadth saves them from reimplementing each architecture by hand. A shared dataset API, loss functions, and evaluation helpers mean you can run different models against the same preprocessing pipeline and compare results directly. The code is documented against the papers it reproduces, which makes it a practical reference for rerunning experiments and a base to extend with your own EEG data.