EEG-DL provides a collection of deep learning algorithms for classifying electroencephalography signals, supporting architectures such as CNN, LSTM, GRU, ResNet, DenseNet, Transformer, and Graph Convolutional Networks. It is designed for brain-computer interface research and neuroscience applications including motor imagery classification, and is built specifically for EEG data processing and classification tasks.