Capstone project for 3252 - Big Data Tools of the Data Science Fundamental program at the University of Toronto SCS - Fall 2017. The report was written to demonstrate how popular big data tools (i.e. Spark, Hadoop, MQTT, etc.) could be used to create a data intensive EEG (brain wave) application using the Google Cloud Platform (GCP).
See attached PDF for full report describing background, methodology, and results.
The MusePython.py library provides a way to send Muse EEG data to the cloud over UDP.
There is also Muse-LSL which provides another set of scripts that use the newer 2016 Muse headset. For my purposes, I have an older Muse headset, so I went with the MusePython.py library as referenced on the InteraXon developer page
LSL (Lab Streaming Layer) is a suite of software utilities for the unified collection and measurement of time series in research experiments that handles both the networking, time-synchronization, (near-) real-time access as well as optionally the centralized collection, viewing and disk recording of the data.' [Source]. The software was developed at the Swartz Centre for Computational Neuroscience at the University of San Diego [link].
EEGrunt is a collection of EEG analysis tools, written in Python. It is compatible with the original Muse headset from 2014 and can be used to run analysis on streaming EEG data.