EEG-Conformer is a deep learning model designed for EEG (electroencephalography) decoding and classification. It combines one-dimensional convolutional layers to extract local temporal and spatial features with a self-attention module to capture global correlations within those features. The model also provides visualization through class activation mapping projected onto brain topography. It has been integrated into the braindecode toolbox and benchmarks on BCI competition datasets.