Data Visualization in Python: Visualizing EEG Brainwave Data

David Landup
Jovana Ninkovic


Electroencephalography (EEG) is the process of recording an individual's brain activity - from a macroscopic scale. It's a non-invasive (external) procedure and collects aggregate, not individual neuronal data. This by all means doesn't mean the procedure is of low quality or inaccurate. What this means is that we see activation data of huge clumps of neurons, corresponding to a singular electrode placed in a certain area.

To collect data on individual neurons, we'd need an invasive technology, which inserts channels directly into the brain, which are in physical contact with the neurons themselves. By using EEG and collecting data from a bunch of neurons that fire together - we've got a fairly effective way to correlate neuron activation to certain stimuli without having to perform invasive surgery on a patient.

We'll be scratching the surface of what EEG is to gain a basic intuition on how it works and how we can interpret the data, before performing any visualizations.

More specifically - we'll be working with two datasets:

The first dataset was created in a study trying to figure out whether EEG correlates with genetic predisposition to alcoholism, while the second was created to figure out whether EEG correlates with the level of confusion of a student while watching MOOC clips of differing complexity.

Both of these tasks are best done with machine learning algorithms, which are great making inference from data. However, the first step in any such project would be to properly explore the data visually, through data visualization techniques.

Note: This Guided Project is combined from parts of our Data Visualization in Python and Data Visualization in Python with Matplotlib and Pandas courses, and additionally made available as a standalone project. For the full experience, please enroll into the respective course(s).

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Turn Theory Into Practice

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Last Updated: Mar 2022

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