The repository "EEG_fNRIS_AD" is a project from UC Berkeley's Introduction to Neurotechnology course. It focuses on analyzing EEG (Electroencephalography) and fNIRS (Functional Near-Infrared Spectroscopy) data to detect early signs of Alzheimer's Disease (AD). The project employs machine learning techniques to identify biomarkers indicative of cognitive decline, aiming to facilitate early diagnosis and personalized interventions.
The repository is organized as follows:
- DataBase/: Contains datasets used for analysis.
- EEG_fNIRS/: Includes scripts and resources related to EEG and fNIRS data processing.
- model.ipynb: A Jupyter Notebook detailing the machine learning model development and evaluation.
- requirements.txt: Lists the Python dependencies required to run the project.
- .gitignore: Specifies files and directories to be ignored by Git.
- LICENSE: The project's licensing information.
- README.md: Provides an overview and documentation of the project.
To set up the project locally:
-
Clone the repository:
git clone https://github.com/NikoHems/EEG_fNRIS_AD.git cd EEG_fNRIS_AD -
Install the required dependencies:
pip install -r requirements.txt
-
Data Preprocessing:
- Navigate to the
EEG_fNIRS/directory for scripts related to data preprocessing. - Ensure that the datasets are correctly placed in the
DataBase/directory.
- Navigate to the
-
Model Training and Evaluation:
- Open the
model.ipynbJupyter Notebook to follow the steps for training and evaluating the machine learning models.
- Open the
Contributions to enhance the project are welcome. Please fork the repository, create a new branch for your feature or bug fix, and submit a pull request for review.
This project is licensed under the MIT License. Refer to the LICENSE file for more information.
For questions or collaboration opportunities, please open an issue in the repository or contact the project maintainers directly.