EEG Annotation Tool

EEG annotation tool written in Python and PyQt6. It uses MNE Python for EEG signal reading and processing.

Note: Current version of this app supports .eeg,.edf and .fif formats.

Setting up your enviroment (Windows/Linux)

1. Clone the repo: git clone https://github.com/FrexG/eeg_annotator.git
2. Create a conda evironment (prefrable): conda create -n eeg_annotator python=3.11.
3. conda activate eeg_annotator
4. Install Required libs: pip3 install -r requirements.txt

Usage

1. Change directory to eeg_annotator and run the following command to start the application:

   python3 main.py
   

2. Load your EEG data by clicking on the "Open" button.

3. Use the "Bounding Rect Annotation" feature to annotate your data by pressing the Label button, left click and drag your mouse on the canvas to draw a rectangle selection.

4. You can move and resize the rectangle as you wish. When finish press the Label button again to provide annotation to the selected region.

5. Save your annotations by clicking on the Save Annotations button.

6. Use the left/right arrow keys to move through the EEG signal in time.

You can also change the bipolar montage type or add/remove labels in the config.py file

Annotation

• The annotation file is a .json file with the same name as the EEG file and is saved in the same directory as the source EEG file.
• Each annotation file contains a list of dictionaries each describing a single selected region.
• new If annotation file already exists for a given reading they will be rendered upon opening.
• to add new labels edit config.py and add the new labels to diagnosis tuple.

# example_annotation.json

[
    {
        "channels": [
            "P4-O2"
        ],
        "start_time": 1002,
        "stop_time": 1006,
        "onset": "GSCZ"
    },
    {
        "channels": [
            "F4-C4",
            "C4-P4",
            "P4-O2"
        ],
        "start_time": 530,
        "stop_time": 539,
        "onset": "FSCZ"
    },
    {
        "channels": [
            "Fp2-F4",
            "F4-C4",
            "C4-P4",
            "P4-O2"
        ],
        "start_time": 1041,
        "stop_time": 1045,
        "onset": "OSCZ"
    }
]

License

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

Copyright 2023, Ethiopian Artificial Intelligence Institute