emg-gui

1. Overview

1.1 Introduction

A real-time visual engine for acquiring, recording, and exploring raw and filtered EMG signals.

1.2 Installation

EMG-GUI can be installed from pypi using pip:

pip install emg-gui

1.3 How to Run

TODO

2. Architecture and Project Structure

2.1 Architecture

The application separates sensor acquisition, interface controls, signal processing, and GPU rendering. Sensor polling and board stream control run on a dedicated thread, while Qt interface updates, filtering, and ModernGL rendering currently run on the main thread.

EMG GUI
├── app.py
├── Acquisition
│   ├── DataSource
│   └── SensorWorker
│       └── RingBuffer
├── UI
│   ├── EMGVisualizerWindow
│   └── EMGControlPanel
├── Processing
│   ├── EMG filtering
│   └── Window functions
└── Visualization
    ├── EMGOpenGLWidget
    ├── TimeSeriesRenderer
    ├── SpectrogramRenderer
    ├── TicksRenderer
    └── TextRenderer

The DataSource provides hardware-independent acquisition from either an OpenBCI board or a playback recording. The current application entry point uses OpenBCIBoard by default, with playback available as an alternate source. SensorWorker reads EMG samples into a fixed-size ring buffer, and EMGOpenGLWidget owns duplicate-snapshot checks, snapshot filtering, and ModernGL rendering. TimeSeriesRenderer and SpectrogramRenderer are connected to the live rendering path. Channel counts come from the selected data source, so synthetic and real boards can expose different numbers of EMG channels. The display window is sample-based, while the rendered layout is derived from the OpenGL widget size and the selected EMG channel count.

See the architecture guide for the detailed component hierarchy, data flow, and thread interaction sequences.

2.2 Directory Layout

Inside this project, you'll see the following folders and files:

/
├── docs/
│   ├── diagrams/
│   │   ├── Ability_Logo.svg
│   │   └── interface-contracts.svg
│   └── architecture.md
├── src/
│   └── emg_gui/
│       ├── app.py
│       ├── acquisition/
│       │   ├── data_source.py
│       │   ├── dataset_files.py
│       │   └── sensor_worker.py
│       ├── config/
│       │   ├── constants.py
│       │   └── Roboto-Black.ttf
│       ├── core/
│       │   ├── enums.py
│       │   ├── logger.py
│       │   └── types.py
│       ├── processing/
│       │   ├── filter.py
│       │   └── window_functions.py
│       ├── ui/
│       │   ├── control_panel.py
│       │   └── window.py
│       └── visualizer/
│           ├── shaders/
│           │   ├── shader_loader.py
│           │   ├── spec/
│           │   │   ├── vertex.glsl
│           │   │   └── fragment.glsl
│           │   ├── text/
│           │   │   ├── vertex.glsl
│           │   │   └── fragment.glsl
│           │   ├── ticks/
│           │   │   ├── vertex.glsl
│           │   │   └── fragment.glsl
│           │   └── time_series/
│           │       ├── vertex.glsl
│           │       └── fragment.glsl
│           ├── open_gl_widget.py
│           ├── spectrogram_renderer.py
│           ├── text_renderer.py
│           ├── ticks_renderer.py
│           └── time_series_renderer.py
├── tests/
├── CONTRIBUTING.md
├── pyproject.toml
└── README.md

3. Contributing

Contributions are welcome. See the contributing guide for development and linting guidelines.

4. Acknowledgements

4.1 BrainFlow

This project uses BrainFlow for biosignal acquisition and hardware integration. BrainFlow provides a uniform API for acquiring, parsing, and analyzing EEG, EMG, ECG, and other biosensor data across a wide range of supported boards, including OpenBCI devices. BrainFlow is distributed under the MIT License.

4.2 ModernGL Spectrogram

This project uses nickcercone/spectrogram as a reference for parts of its ModernGL spectrogram rendering implementation. The referenced project demonstrates real-time audio spectrogram visualization using ModernGL and is distributed under the MIT License.

4.3 Text Rendering

This project adapts the code in How to render text with PyOpenGL? by exiled (question) and Rabbid76 (answer) for its ModernGL text renderer. The referenced contributions demonstrate text rendering with PyOpenGL and are distributed under the CC BY-SA 4.0 License.

5. Tech Debt

1. Add setup and installation instructions to this readme.
2. Create new repo-emg-ability (create import and remove run.sh).
3. Folder portability use EMGFlow & system path reference.
4. Add legends for raw and filtered emg in timeseries renderer.
5. Processing latency in the performance metrics.
6. Improve the GUI toggle controls and expose their options in the left sidebar.
7. Add timer and countdown visualizations.
8. Move signal processing from the GUI thread to a dedicated processing thread.
9. Move the complete data-source lifecycle into the sensor thread, including initialization, streaming, recording, stopping, and release.