Amir-Hofo/EEGNet

This project is a straightforward PyTorch port of the 2018 paper 'EEGNet: A Compact Convolutional Neural Network for EEG-based Brain-Computer Interfaces'. While EEGNet is a foundational architecture in the BCI space, this specific repository lacks a moat. With only 49 stars and 0 velocity over a two-year period, it functions more as a personal reference or a tutorial rather than a production-grade library. It faces heavy competition from more comprehensive frameworks like 'Braindecode', which provides optimized implementations of EEGNet alongside various other architectures, data loaders, and preprocessing pipelines. Furthermore, the original authors provided a Keras implementation which remains a primary reference. There is no proprietary data, unique optimization, or community lock-in here. Frontier labs (OpenAI, Anthropic) have little interest in niche BCI signal processing at this stage, but the project is already displaced by more mature open-source EEG libraries that offer better maintenance and feature parity.