wjq-learning/CBraMod

What it solves

CBraMod is designed to improve EEG (electroencephalogram) decoding for clinical and Brain-Computer Interface (BCI) applications. It provides a foundation model that can be pretrained on large EEG datasets and then fine-tuned for specific downstream tasks.

How it works

The project implements a "Criss-Cross Brain Foundation Model." It allows users to pretrain the model on a large dataset to learn general brain activity patterns and then apply a custom classifier (such as a linear layer or MLP) on top of the output to decode specific brain signals for various applications.

Who it’s for

Researchers and developers working in neuroscience, BCI, and clinical EEG analysis who need a robust starting point for decoding brain signals.

Highlights

• Foundation Model Approach: Uses a pretraining and fine-tuning pipeline to handle EEG decoding across different applications.
• ICLR 2025: The model and methodology were accepted for presentation at the ICLR 2025 conference.
• Pretrained Weights: Ready-to-use weights are available on Hugging Face for faster deployment.
• Flexible Fine-tuning: Supports custom downstream datasets and classifiers for specific decoding tasks.