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Cbramod

Cbramod is an open-source Python research codebase for CBraMod, a pretrained EEG foundation model used for EEG decoding. It includes code to pretrain the model, fine-tune it on downstream datasets, and use its output with a classifier; a pretrained checkpoint is linked from the repository.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Cbramod — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when you need research code for EEG model pretraining or fine-tuning, or want to build a task-specific classifier on top of the pretrained model.

Who it is for

  • EEG researchers working on clinical or brain-computer interface decoding.
  • Developers and researchers adapting pretrained EEG representations to downstream classification tasks.

Documented uses

  • Pretrain CBraMod on an EEG dataset, including a custom pretraining dataset.
  • Fine-tune the model on a selected or custom downstream dataset.
  • Load the pretrained checkpoint and attach a classifier for downstream EEG classes.
Before you choose

Requirements & limits

The repository is public and MIT-licensed. The README links to a pretrained checkpoint on Hugging Face.

  • The project provides model and training code rather than a packaged clinical or BCI application.
  • The README’s example classifier uses an input shape of (8, 22, 4, 200) and four output classes; task-specific data and classifier choices may differ.
  • Python, PyTorch and the repository’s listed requirements are part of the documented setup.
  • The README links to a pretrained checkpoint, as well as pretraining and fine-tuning entry-point scripts.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up the CBraMod Python environment

Install the documented Python dependencies before using the repository’s pretraining or fine-tuning code.

Before you start

  • Python
  • PyTorch
  • The CBraMod repository and its requirements.txt file

Steps

  1. Install Python.

  2. Install PyTorch using the official PyTorch installation guidance.

  3. From the repository, install the listed requirements with the command below.

    Documented command
    pip install -r requirements.txt

Expected outcome

The documented Python, PyTorch and repository requirements are installed for proceeding to the repository’s pretraining or fine-tuning stage.

Guide limitations

  • The setup instructions do not specify Python or PyTorch versions.
  • Installing requirements does not itself pretrain or fine-tune a model.
  • The repository’s subsequent training commands and dataset preparation are separate stages.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
8 October 2026
Original website title
wjq-learning/CBraMod · Official homepage title · retained in its source language
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