EEG Alzheimer's Detection Using LLM and ML Classifier

This project is designed to analyze EEG data and predict the likelihood of Alzheimer's disease using a combination of a Large Language Model (LLM) and a Machine Learning (ML) classifier. The tool provides an interface for uploading EEG data and receiving diagnostic predictions.

Features

EEG Data Upload: Easily upload EEG files for analysis.

Integrated Analysis: Utilizes both LLM and ML models to enhance prediction accuracy.

User Interface: Built with the "gradio" package for a seamless user experience.

Integration with OpenAI and NVIDIA: This project utilizes NVIDIA's Palmyra-Med-70B-32K API in collaboration with OpenAI's advanced language models to enhance the analysis and prediction capabilities of the system.

Installation Clone the Repository:

bash

git clone https://github.com/yourusername/your-repo-name.git cd your-repo-name Install Dependencies:

Ensure you have Python installed. Install the necessary packages using the provided text file: bash

pip install -r requirements.txt Set Up API Key:

Obtain an API key from nVIdia by making an acount. Create a .env file in the project folder and add your API key: SECRET_API_KEY=your_api_key_here Usage Run the Main Function:

Execute the main script to start the application: bash

python main.py Interface Interaction:

Use the user interface to upload EEG data and receive predictions. Contributing We welcome contributions from the community. Please feel free to submit issues or pull requests.

License This project is licensed under the MIT License. See the LICENSE file for details.

Contact For questions or support, please contact Soudeh (Venus) Mostaghimi at [email protected], or Trisha Mendoza at [email protected], or Blanca Romer at [email protected].