EEG Visualizer

A web-based EEG analysis tool built with MNE-Python and Streamlit.

Features

• Load EDF/EDF+ files with automatic validation
• Import event markers from CSV or JSON files
• Interactive channel selection and bad channel marking
• Real-time filtering (high-pass, low-pass, notch)
• Topographic brain maps
• Power spectral density analysis
• Regional brain activity analysis
• Export processed data and visualizations

Quick Start

Installation

git clone https://github.com/giovaniemotiv/eeg-visualizer.git
cd eeg-visualizer
pip install -e src/

Running the Application

Method 1: Using Python module (Recommended)

py -m streamlit run src/eegviz/app/main.py

Method 2: Direct streamlit command

streamlit run src/eegviz/app/main.py

Method 3: With specific port

py -m streamlit run src/eegviz/app/main.py --server.port 8502

Access the Application

Open your browser and go to http://localhost:8501 (or the port shown in the terminal output).

Troubleshooting

If streamlit command is not found:

• Use Method 1 with py -m streamlit
• Make sure Streamlit is installed: pip install streamlit

If port is already in use:

• Use Method 3 to specify a different port
• Or the application will automatically find an available port

Project Structure

src/eegviz/
├── app/main.py        # Streamlit application
├── core/              # Session management and validation
├── ui/                # UI components
├── io/                # File loading
├── analysis/          # Signal analysis
├── viz/               # Visualization
└── export/            # Data export

File Formats

EDF Files

Standard EEG recording format with annotation support.

Marker Files

CSV format:

latency,duration,type
1.5,0.5,stimulus
3.2,0.2,response

JSON format:

{
  "Markers": [
    {
      "startDatetime": "2024-01-01T10:00:01.500Z",
      "endDatetime": "2024-01-01T10:00:02.000Z",
      "label": "stimulus"
    }
  ]
}

Development

Dependencies

Core requirements:

• mne>=1.10.1
• streamlit>=1.33
• matplotlib>=3.7
• numpy>=1.24
• pandas>=2.0

Testing

pip install -e src/[dev]
pytest tests/

License

MIT License - see LICENSE file for details.

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

Documentation

• Installation Guide
• User Guide
• Examples