Copyright (C) 2025 ETH Zurich, Switzerland. SPDX-License-Identifier: Apache-2.0. See LICENSE file at the root of the repository for details.
This directory contains the implementations of the deep learning models used in the BioFoundation project. Each model is defined as a PyTorch nn.Module and is designed to be configurable and extensible for various research tasks.
- FEMBA: A lightweight EEG model designed for both pretraining and fine-tuning tasks. For a more detailed description of the model check the documentation.
- LUNA: An efficient EEG model specifically designed for handling different types of electrode configurations. For a more detailed description of the model check the documentation.
- TinyMyo: A 3.6M-parameter Transformer-based foundation model for surface EMG (sEMG). It is pretrained on >480 GB of EMG data and optimized for ultra-low-power, real-time deployment, including microcontrollers (GAP9) where it achieves an inference time of 0.785 s, energy of 44.91 mJ and power envelope of 57.18 mW. For a more detailed description of the model check the documentation.