This project aims to classify mental attention states — focused, unfocused, and drowsy — using EEG signals and machine learning techniques. By analyzing EEG data collected from EMOTIV devices, the goal is to develop a reliable classification model that can distinguish different mental states, contributing to applications in cognitive enhancement, neurofeedback, and mental state monitoring.
| Name | Major | University |
|---|---|---|
| Kieu Thi Ngoc Vui | Data Science | University of Science (VNUHCM) |
| Nguyen Ngoc Thanh Thu | Data Science | University of Science (VNUHCM) |
| Phan Binh Phuong | Data Science | University of Science (VNUHCM) |
| Huynh Thao Quynh | Data Science | University of Science (VNUHCM) |
After performing the git add . command, the git commit message should follow this structure:
git commit -m "[folder/file updated] - [task description]"
Example:
git commit -m "EEG_Classifier/MentalStateClassification.ipynb - Update feature extraction."
Task description should provide enough information for other members to understand what was updated or changed, e.g., fixing bugs, adding features, refactoring code.
After that, use the git push command to push into the GitHub repository.
| Folder | Description |
|---|---|
| Data | Contains the original dataset used for training and testing. |
| EEG_Classifier | Source code for data preprocessing, feature engineering, model training, and performance visualization, including metrics like confusion matrices and ROC curves. |
| Reports | Documented reports and presentations summarizing the project findings. |
| Setup | Contains the environment setup files and dependencies required to run. |