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Cerebral Cortex Cloud Platform

Cerebral Cortex is the big data cloud companion of mCerebrum designed to support population-scale data analysis, visualization, model development, and intervention design for mobile sensor data.

CerebralCortex provides an interface to retrieve/store mobile sensor raw data and metadata.

This repository is allows you to install and evaluate the Cerebral Cortex platform.

Python Source Code Repos


We have renamed following repositories.

  • CerebralCortex-Platform -> CerebralCortex
  • CerebralCortex - > CerebralCortex-Kernel


  • 2018.03.18 Cerebral Cortex Cloud Platform
    • Grafana visualization support
    • Jupyter Notebook analysis platform
    • Cerebral Cortex 3.0.0
    • File system based storage architecture
    • Ability to collect data from mCerebrum app (Note: mCerebrum is not compatiable for CerebralCortex 3.0.0 yet.)


This software is intended for informational and demonstration purposes only and is not designed to diagnose, treat, cure, prevent, or track disease or health states. No content provided in this software is intended to serve as a substitute for any kind of professional (e.g., medical) advice.

Installation Instructions

The Cerebral Cortex platform can be installed and tested on any of the three major platforms: Linux, Mac OS X, and Windows. The following instructions will walk you through installing the dependencies necessary to run Cerebral Cortex. The Docker images take approximately 8 minutes to download, build, and install on a 1000 Mbit/second internet connection. We are continually working on improving the speed with which everything downloads and installs.

Linux: (Ubuntu 18.04)

These steps are performed from the command line and do not need a graphical interface.

  1. Install Docker
sudo apt-get update

sudo apt-get install \
  apt-transport-https \
  ca-certificates \
  curl \

curl -fsSL | sudo apt-key add -

sudo add-apt-repository \
  "deb [arch=amd64] \
  $(lsb_release -cs) \

sudo apt-get update
sudo apt-get install docker-ce
  1. Install docker-compose
sudo curl -L "$(uname -s)-$(uname -m)" -o /usr/local/bin/docker-compose

sudo chmod +x /usr/local/bin/docker-compose

Please consult the Docker site if you face any installation errors for step 1 and 2.

  1. Download or clone this CerebralCortex repository.
git clone
cd CerebralCortex
docker-compose up -d

In case you encounter an error relating to permissions, add your user to the docker group. sudo usermod -aG docker $USER

Mac OS X:

  1. Install Docker

  2. Install docker-compose

sudo curl -L "$(uname -s)-$(uname -m)" -o /usr/local/bin/docker-compose

sudo chmod +x /usr/local/bin/docker-compose

Please consult the Docker site if you face any installation errors for step 1 and 2.

  1. Download or clone this CerebralCortex repository.
git clone
cd CerebralCortex
  1. Update following configs in docker-compose.yml. Comment HOSTNAME_COMMAND: "route -n | awk '/UG[ \t]/{print $$2}'", uncomment KAFKA_ADVERTISED_HOST_NAME:, and put your public ip address there.
      HOSTNAME_COMMAND: "route -n | awk '/UG[ \t]/{print $$2}'"
  1. Run docker-compose
docker-compose up -d

Windows 10:

  1. Install Docker

  2. Download the CerebralCortex repository.
    Download location:

  3. Extract the file and open it in Explorer

  4. Open PowerShell and run the following commands

PS C:\Users\hnat> cd Downloads\CerebralCortex-master
PS C:\Users\hnat\Downloads\CerebralCortex-master>

PS C:\Users\hnat\Downloads\CerebralCortex-master> docker-compose up

Note: Approve the Docker's requests for accessing a shared drive

Launch Cerebral Cortex

Open the following link to view the Cerebral Cortex launch page: http://localhost/


This version Cerebral Cortex is not configured with SSL/TLS support out of the box and should NOT be utilized to collect human subject data for research. It is currently designed for development, testing, and training purposes. We are working on a secure-by-default configuration for Cerebral Cortex to avoid this problem in the future. If you would like to secure this system as is, you will need to configure NGINX's SSL/TLS support manually.

Optional: Steps to Explore Underlying Services

Use the commands below to confirm that everything is installed and working correctly. Docker-Compose commands can be used to interact with Cerebral Cortex's containers. The following commands lists the the status of all the services used by CerebralCortex. Most containers for Cerebral Cortex will report a (healthy) state as long as they are running.

docker-compose ps

The above commands display the status of all the services as such as the example shown below.

          Name                         Command                       State                                 Ports                       
cerebralcortex-apiserver    / /         Up (health: starting)   443/tcp, 80/tcp                                   
cerebralcortex-grafana      /                          Up            >3000/tcp                            
cerebralcortex-influxdb     / influxd           Up            >8086/tcp                            
cerebralcortex-jupyterhub   sh -c chown -R md2k /cc_da ...   Up (health: starting)>8000/tcp                           
cerebralcortex-kafka                   Up            >9092/tcp                            
cerebralcortex-mysql mysqld      Up            >3306/tcp, 33060/tcp                 
cerebralcortex-nginx        nginx -g daemon off;             Up (health: starting)>443/tcp,>80/tcp          
cerebralcortex-zookeeper    /bin/sh -c /usr/sbin/sshd  ...   Up            >2181/tcp, 22/tcp, 2888/tcp, 3888/tcp

Import and analyze the data

Data is automatically imported into the system when mCerebrum is connected to the cloud platform. You can also initiate a replay of the data in the following way.

Connecting mCerebrum

mCerebrum is our mobile application for Android that collects, processes, and transmits data to Cerebral Cortex. It is available here: for download and installation. This app will let you authenticate with the default user and password md2k:md2k and your DNS entry for the Cerebral Cortex server. From this point, it will automatically download the configuration file one the system and start the setup process. The phone will upload data every 15 minutes data sources that are not raw sensor data and every hour for high-rate raw sensors. These will be reflected in the data processing and visualization interfaces

Current limitations:

  • The provide mCerebrum configuration in the minio/configuration folder need to be modified to support the proper data upload URL. Once you extract this zip file, edit the mCerebrum/org.md2k.datakit/config.json file and change the upload:url to your DNS entry for Cerebral Cortex. Recompress the mCerebrum folder and overwrite the current zip file.
  • If mCerebrum does not properly connect to your provided DNS host running Cerebral Cortex, you MUST force stop the app on the phone before relaunching and logging into Cerebral Cortex.

Both of these bugs are logged in the tracking system and we will fix them when we can but this should not prevent you from testing out the platform.

Visualizing and Analyzing Your Data

Cerebral Cortex provides two mechanisms to visualize and analyze your data. First, a user-centric interface is provided by the Grafana project which can be utilized to plot and explore Cerebral Cortex data streams. Second, a code-centric interface is provided by the Jupyter project and allows a user to write Python 3 code to interact with the Cerebral Cortex kernel.

Visualization of data with Grafana

Open this link in your web browser http://localhost/grafana/login to visualize your data.

  1. The default login and password are both admin.


  1. Once you authenticate, you will see the following screen.

Grafana Main

  1. Select the Home dropdown at the top-left of the screen and choose the MD2K dashboard.

Grafana Visualization

This is a pre-built visualization that provides some examples of the various types of displays that are possible.

For example:

  • Smartphone Accelerometer/Gyrometers/Magentometer
  • Battery levels of connected devices
  • Step count as determined from the smartphone

You may create additional dashboards to visualize all of the raw and processed data.

Analyzing your data with Jupyter Notebooks

Open this link in your web browser http://localhost/jupyterhub/hub/login to interact and analyze your data.

  1. A login screen will be shown as follows.

The username and password are both md2k. The warning shown is because this site is running locally on your machine and is not secured by a security certificate. There is no data leaving the machine and going across the internet.

Jupyter Hub

  1. A file browser will appear after successful authentication and you should choose the cc_demo folder.

Jupyter Files

Jupyter Demo

  1. Click on the CerebralCortex_Basic_Usage.ipynb and it will open in a new tab. This provides an overview of how to utilize Cerebral Cortex and visualize some data.

Jupyter Cerebral Cortex Demo

This example notebook demonstrates the following:

  • Import CerebralCortex libraries and loading configurations
  • Get all users of a study
  • Get all streams of a user
  • Get days when a stream has data available
  • Get a stream's raw data and metadata
  • Plot stream raw data

Creating your own scripts

  1. Authenticate with user credentials
  2. Click on Files tab
  3. Click on new and select pySpark (Spark 2.3.2) (Python 3) to create a new Python script.

Computing features

The CerebralCortex-DataAnalysis repository is available as a Docker container (cerebralcortex-dataanalysis). This repository contains the code to compute features on the data. These features are located in the core/feature directory.

The following features have been validated by our team and are considered stable with the remaining features still under development. Please have a look at the documentation for each of the above features to get more insight into their functionality. Sensors/features in parentheses should be considered dependencies to compute the specified feature.

Stable Features

  • phone_features (Smartphone)
  • phone_screen_touch_features (Smartphone)
  • puffMarker (MotionSenseHRV)

Features Under Development

  • activity and posture classification (MotionSenseHRV)
  • typing features (MotionSenseHRV)
  • rr_interval (MotionSenseHRV)

Running Data Analysis

The container can be run against any temporal subset of data within the system after you have collected and/or ingested data. From the CerebralCortex-Platform folder run the following command. The console will output a log of the individual modules debug messages. Additionally, this container will launch at the initial docker-compose startup and process any data already in the system.

Format: docker-compose run dataanalysis sh StartDate(YYYYMMDD) EndDate(YYYYMMDD)

docker-compose run dataanalysis sh 20181101 20190101

Running Data Analysis in the Jupyter notebook

The Jupyter notebook also has the DataAnalysis code in the DataAnalysis folder. In this folder there is a sample notebook Simple_driver.ipynb that can be used to execute features described above. In this sample, the phone based features are computed. Change the input parameters in the notebook to compute the desired features.

Starting and stopping Cerebral Cortex

Stop Cerebral Cortex

docker-compose down

Start Cerebral Cortex

docker-compose up

Delete all data and container data for Cerebral Cortex

docker-compose stop
docker-compose rm

make clean


  1. I'm stuck, where do I get help?

Please look for more information or ask for help here:

  1. How do I find out about new releases and software announcements

    Please sign up for our software announcement mailing list:

  2. System requirements

These are the minimum recommended system requirements for running Cerebral Cortex.

  • 2-4 core CPU
  • 16+ GB RAM
  • 10GB disk + enough to support the total data collection from mCerebrum


Please read our Contributing Guidelines for details on the process for submitting pull requests to us.

We use the Python PEP 8 Style Guide.

Our Code of Conduct is the Contributor Covenant.

Bug reports can be submitted through JIRA.

Our discussion forum can be found here.


We use Semantic Versioning for versioning the software which is based on the following guidelines.

MAJOR.MINOR.PATCH (example: 3.0.12)

  1. MAJOR version when incompatible API changes are made,
  2. MINOR version when functionality is added in a backwards-compatible manner, and
  3. PATCH version when backwards-compatible bug fixes are introduced.

For the versions available, see this repository's tags.


Link to the list of contributors who participated in this project.


This project is licensed under the BSD 2-Clause - see the license file for details.


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