BrainflowCyton 0.2.2

Python wrapper for BrainFlow API + Cyton SDK for collecting realtime or offline (SDCard) EEG, EMG, EKG data.

BrainflowCytonEEGWrapper

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

pip install BrainflowCyton

Optional: Audio Utilities

If you want to use the Audio class for EEG sonification, install with the audio extra:

pip install BrainflowCyton[audio]

This requires PortAudio to be installed on your system:

macOS:

brew install portaudio

Ubuntu/Debian:

sudo apt-get install portaudio19-dev

Windows: PortAudio should be installed automatically with the package.

Or for development:

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):

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:

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

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.

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

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

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

# Install dev dependencies
uv sync --all-extras --dev

# Run tests
uv run pytest tests/ -v

Contributing

Contributions are welcome! Please see 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