| Name | BrainflowCyton JSON |
| Version |
0.2.2
JSON |
| download |
| home_page | None |
| Summary | Python wrapper for BrainFlow API + Cyton SDK for collecting realtime or offline (SDCard) EEG, EMG, EKG data. |
| upload_time | 2025-10-20 11:49:27 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | MIT |
| keywords |
biosignals
brainflow
cyton
eeg
ekg
emg
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# BrainflowCytonEEGWrapper
[](https://github.com/zeyus-research/BrainflowCytonEEGWrapper/actions/workflows/test.yml)
[](https://badge.fury.io/py/BrainflowCyton)
[](https://pypi.org/project/BrainflowCyton/)
[](https://opensource.org/licenses/MIT)
This is particularly aimed at the OpenBCI Cyton/Cyton + Daisy biosensing board.
It allows for low-level board commands such as configuring channels and setting sample rate, making it easier to configure the way you retrieve data from the board.
## Status
Fully functional, working with Cyton and Cyton + Daisy.
Todo:
- [ ] Include script to process SDCard data (exists but in a different repo)
- [ ] Make the interface more consistent
- [ ] Bake in multithreading support for easier parallel data collection + processing
- [ ] ???
## Details
Default channels coming in with cyton and daisy with the ultracortex mk4 are:
- 0: "pkg"
- 1: "Fp1"
- 2: "Fp2"
- 3: "C3"
- 4: "C4"
- 5: "P7"
- 6: "P8"
- 7: "O1"
- 8: "O2"
- 9: "F7"
- 10: "F8"
- 11: "F3"
- 12: "F4"
- 13: "T7"
- 14: "T8"
- 15: "P3"
- 16: "P4"
- 17: "AX" (accelerometer x)
- 18: "AY" (accelerometer y)
- 19: "AZ" (accelerometer z)
- 31: "marker" (this can be used to put event markers in the EEG data, which is extremely useful, BUT the accelerometer will be disabled)
## Installation
```bash
pip install BrainflowCyton
```
### Optional: Audio Utilities
If you want to use the Audio class for EEG sonification, install with the `audio` extra:
```bash
pip install BrainflowCyton[audio]
```
This requires PortAudio to be installed on your system:
**macOS:**
```bash
brew install portaudio
```
**Ubuntu/Debian:**
```bash
sudo apt-get install portaudio19-dev
```
**Windows:** PortAudio should be installed automatically with the package.
Or for development:
```bash
git clone https://github.com/zeyus-research/BrainflowCytonEEGWrapper
cd BrainflowCytonEEGWrapper
pip install -e .
```
## Features
- Low-level control of OpenBCI Cyton/Cyton + Daisy boards
- Support for custom sample rates (250Hz - 16kHz)
- SD card recording and real-time streaming
- Channel configuration (gain, input type, bias, SRB settings)
- EMG channel support
- Event markers/tags for experiment synchronization
- Built-in filtering (bandpass, lowpass, 50Hz noise removal)
- Context manager support for automatic resource cleanup
- Type hints for better IDE support
- Comprehensive test suite
## Usage examples
### Read data from a dummy board in real time (recommended pattern)
Using context manager (automatically handles cleanup):
```python
from BrainflowCyton.eeg import EEG
from time import sleep
# Context manager automatically calls prepare() and stop()
with EEG(dummyBoard=True) as eeg:
eeg.start_stream(sdcard=False)
while True:
try:
sleep(0.5)
data = eeg.poll()
print(f"Got {data.shape[1]} samples")
except KeyboardInterrupt:
break
```
Or manually managing the connection:
```python
from BrainflowCyton.eeg import EEG
from time import sleep
eeg = EEG(dummyBoard=True)
eeg.start_stream(sdcard=False)
while True:
try:
sleep(0.5)
data = eeg.poll()
except KeyboardInterrupt:
eeg.stop()
break
```
### Read data from a real board in real time
```python
from BrainflowCyton.eeg import EEG
from time import sleep
eeg_source = EEG()
eeg_source.start_stream(sdcard = False)
while True:
try:
sleep(0.5)
data = eeg_source.poll()
except KeyboardInterrupt:
eeg_source.stop()
```
### Set a custom sample rate
*Note: to use sample rates above 250, an SDCard is required, streaming is limited to 250 Hz.*
```python
from BrainflowCyton.eeg import EEG, CytonSampleRate
from time import sleep
eeg_source = EEG()
eeg_source.start_stream(sdcard = True, sr = CytonSampleRate.SR_1000)
while True:
try:
sleep(0.5)
data = eeg_source.poll()
except KeyboardInterrupt:
eeg_source.stop()
```
### Enable logging
```python
from BrainflowCyton.eeg import EEG
import logging
# Configure logging before creating EEG object
EEG.configure_logging(level=logging.DEBUG)
with EEG(dummyBoard=True) as eeg:
eeg.start_stream(sdcard=False)
# Will now see detailed logs
```
### Bandpass the data
```python
from BrainflowCyton.eeg import EEG, Filtering
from time import sleep
# Set the indexes of channels you want to filter
ch_idx = [1, 2, 3, 4, 5, 6, 7]
eeg_filter = Filtering(exg_channels=ch_idx, sampling_rate=250)
with EEG(dummyBoard=True) as eeg:
eeg.start_stream(sdcard=False)
while True:
try:
sleep(0.5)
data = eeg.poll()
if data is not None:
# Apply 8-32 Hz bandpass (alpha + beta band)
filtered_data = eeg_filter.bandpass(data, lowcut=8, highcut=32)
except KeyboardInterrupt:
break
```
## Development
### Running tests
```bash
# Install dev dependencies
uv sync --all-extras --dev
# Run tests
uv run pytest tests/ -v
```
### Contributing
Contributions are welcome! Please see [CONTRIBUTING.md](.github/CONTRIBUTING.md) for guidelines.
## CI/CD
This project uses GitHub Actions for continuous integration and deployment:
- **Tests**: Run on every push/PR across Python 3.9-3.13 and Linux/macOS/Windows
- **Pre-releases**: Automatically published to GitHub releases on main branch commits
- **Releases**: Triggered by version tags (e.g., `v0.3.0`), publishes to PyPI
## License
MIT License - see LICENSE file for details
Raw data
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"description": "# BrainflowCytonEEGWrapper\n\n[](https://github.com/zeyus-research/BrainflowCytonEEGWrapper/actions/workflows/test.yml)\n[](https://badge.fury.io/py/BrainflowCyton)\n[](https://pypi.org/project/BrainflowCyton/)\n[](https://opensource.org/licenses/MIT)\n\nThis is particularly aimed at the OpenBCI Cyton/Cyton + Daisy biosensing board.\n\nIt allows for low-level board commands such as configuring channels and setting sample rate, making it easier to configure the way you retrieve data from the board.\n\n## Status\n\nFully functional, working with Cyton and Cyton + Daisy.\n\nTodo:\n- [ ] Include script to process SDCard data (exists but in a different repo)\n- [ ] Make the interface more consistent\n- [ ] Bake in multithreading support for easier parallel data collection + processing\n- [ ] ???\n\n## Details\n\nDefault channels coming in with cyton and daisy with the ultracortex mk4 are:\n\n- 0: \"pkg\"\n- 1: \"Fp1\"\n- 2: \"Fp2\"\n- 3: \"C3\"\n- 4: \"C4\"\n- 5: \"P7\"\n- 6: \"P8\"\n- 7: \"O1\"\n- 8: \"O2\"\n- 9: \"F7\"\n- 10: \"F8\"\n- 11: \"F3\"\n- 12: \"F4\"\n- 13: \"T7\"\n- 14: \"T8\"\n- 15: \"P3\"\n- 16: \"P4\"\n- 17: \"AX\" (accelerometer x)\n- 18: \"AY\" (accelerometer y)\n- 19: \"AZ\" (accelerometer z)\n- 31: \"marker\" (this can be used to put event markers in the EEG data, which is extremely useful, BUT the accelerometer will be disabled)\n\n## Installation\n\n```bash\npip install BrainflowCyton\n```\n\n### Optional: Audio Utilities\n\nIf you want to use the Audio class for EEG sonification, install with the `audio` extra:\n\n```bash\npip install BrainflowCyton[audio]\n```\n\nThis requires PortAudio to be installed on your system:\n\n**macOS:**\n```bash\nbrew install portaudio\n```\n\n**Ubuntu/Debian:**\n```bash\nsudo apt-get install portaudio19-dev\n```\n\n**Windows:** PortAudio should be installed automatically with the package.\n\nOr for development:\n\n```bash\ngit clone https://github.com/zeyus-research/BrainflowCytonEEGWrapper\ncd BrainflowCytonEEGWrapper\npip install -e .\n```\n\n## Features\n\n- Low-level control of OpenBCI Cyton/Cyton + Daisy boards\n- Support for custom sample rates (250Hz - 16kHz)\n- SD card recording and real-time streaming\n- Channel configuration (gain, input type, bias, SRB settings)\n- EMG channel support\n- Event markers/tags for experiment synchronization\n- Built-in filtering (bandpass, lowpass, 50Hz noise removal)\n- Context manager support for automatic resource cleanup\n- Type hints for better IDE support\n- Comprehensive test suite\n\n## Usage examples\n\n### Read data from a dummy board in real time (recommended pattern)\n\nUsing context manager (automatically handles cleanup):\n\n```python\nfrom BrainflowCyton.eeg import EEG\nfrom time import sleep\n\n# Context manager automatically calls prepare() and stop()\nwith EEG(dummyBoard=True) as eeg:\n eeg.start_stream(sdcard=False)\n while True:\n try:\n sleep(0.5)\n data = eeg.poll()\n print(f\"Got {data.shape[1]} samples\")\n except KeyboardInterrupt:\n break\n```\n\nOr manually managing the connection:\n\n```python\nfrom BrainflowCyton.eeg import EEG\nfrom time import sleep\n\neeg = EEG(dummyBoard=True)\neeg.start_stream(sdcard=False)\n\nwhile True:\n try:\n sleep(0.5)\n data = eeg.poll()\n except KeyboardInterrupt:\n eeg.stop()\n break\n```\n\n### Read data from a real board in real time\n\n```python\nfrom BrainflowCyton.eeg import EEG\nfrom time import sleep\n\neeg_source = EEG()\neeg_source.start_stream(sdcard = False)\n\nwhile True:\n try:\n sleep(0.5)\n data = eeg_source.poll()\n except KeyboardInterrupt:\n eeg_source.stop()\n \n```\n\n### Set a custom sample rate\n\n*Note: to use sample rates above 250, an SDCard is required, streaming is limited to 250 Hz.*\n\n```python\nfrom BrainflowCyton.eeg import EEG, CytonSampleRate\nfrom time import sleep\n\neeg_source = EEG()\neeg_source.start_stream(sdcard = True, sr = CytonSampleRate.SR_1000)\n\nwhile True:\n try:\n sleep(0.5)\n data = eeg_source.poll()\n except KeyboardInterrupt:\n eeg_source.stop()\n \n```\n\n### Enable logging\n\n```python\nfrom BrainflowCyton.eeg import EEG\nimport logging\n\n# Configure logging before creating EEG object\nEEG.configure_logging(level=logging.DEBUG)\n\nwith EEG(dummyBoard=True) as eeg:\n eeg.start_stream(sdcard=False)\n # Will now see detailed logs\n```\n\n### Bandpass the data\n\n```python\nfrom BrainflowCyton.eeg import EEG, Filtering\nfrom time import sleep\n\n# Set the indexes of channels you want to filter\nch_idx = [1, 2, 3, 4, 5, 6, 7]\neeg_filter = Filtering(exg_channels=ch_idx, sampling_rate=250)\n\nwith EEG(dummyBoard=True) as eeg:\n eeg.start_stream(sdcard=False)\n while True:\n try:\n sleep(0.5)\n data = eeg.poll()\n if data is not None:\n # Apply 8-32 Hz bandpass (alpha + beta band)\n filtered_data = eeg_filter.bandpass(data, lowcut=8, highcut=32)\n except KeyboardInterrupt:\n break\n```\n\n## Development\n\n### Running tests\n\n```bash\n# Install dev dependencies\nuv sync --all-extras --dev\n\n# Run tests\nuv run pytest tests/ -v\n```\n\n### Contributing\n\nContributions are welcome! Please see [CONTRIBUTING.md](.github/CONTRIBUTING.md) for guidelines.\n\n## CI/CD\n\nThis project uses GitHub Actions for continuous integration and deployment:\n\n- **Tests**: Run on every push/PR across Python 3.9-3.13 and Linux/macOS/Windows\n- **Pre-releases**: Automatically published to GitHub releases on main branch commits\n- **Releases**: Triggered by version tags (e.g., `v0.3.0`), publishes to PyPI\n\n## License\n\nMIT License - see LICENSE file for details\n",
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