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High level Python module for EEG/EMG/ECG acquisition and distributed streaming for OpenBCI Cyton board.

Project description

Developed by Yeison Nolberto Cardona Álvarez
Andrés Marino Álvarez Meza, PhD.
César Germán Castellanos Dominguez, PhD.
Digital Signal Processing and Control Group | Grupo de Control y Procesamiento Digital de Señales (GCPDS)
National University of Colombia at Manizales | Universidad Nacional de Colombia sede Manizales


High level Python module for EEG/EMG/ECG acquisition and distributed streaming for OpenBCI Cyton board.

GitHub top language PyPI - License PyPI PyPI - Status PyPI - Python Version GitHub last commit CodeFactor Grade Documentation Status

Consist in a set of scripts which deals with the configuration and connection with the board, is compatible with both connection modes supported by Cyton: RFduino (Serial dongle) and WiFi (with the OpenBCI WiFi Shield). These drivers are a stand-alone library that can be used to handle the board from three different endpoints: (i) a Command Line Interface (CLI) with simple instructions configure, start and stop data acquisition, debug stream status and register events markers; (ii) a Python Module with high-level instructions and asynchronous acquisition; (iii) an object-proxying using Remote Python Call (RPyC) for distributed implementations that can manipulate the Python modules as if they were local, this last mode needs a daemon running in the remote host that will be listening connections and driving instructions.

The main functionality of the drivers reside on to serve a real-time and distributed access to data flow, even on single machine implementations, this is achieved by the implementation of Kafka and their capabilities to create multiple topics for classifying the streaming, these topics are used to separate the neurophysiological data from the event markers, so the clients can subscript to a specific topic for injecting or read content, this means that is possible to implement an event register in a separate process that stream markers for all clients in real-time without handle dense time-series data. A crucial issue stays on time synchronization, is required that all system components in the network be referenced to the same local real-time protocol (RTP) server.

Main features

  • Asynchronous acquisition: Acquisition and deserialization is done in uninterrupted parallel processes, in this way the sampling rate keeps stable as long as possible.
  • Distributed streaming system: The acquisition, processing, visualizations and any other system that needs to be feeded with EEG/EMG/ECG real-time data can be run with their own architecture.
  • Remote board handle: Same code syntax for develop and debug Cython boards connected in any node in the distributed system.
  • Command line interface: A simple interface for handle the start, stop and access to data stream directly from the command line.
  • Markers/Events handler: Beside the marker boardmode available in Cyton, a stream channel for the reading and writing of markers are available for use it in any development.

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