The brain generates electrical signals that can be picked up by brain-computer interfaces (BCIs).

A BCI can quantify one's attention level by measuring those signals, known as electroencephalogram (EEG) waves.

We describe our process for constructing a BCI system to break down EEG waves into various frequency sub-bands. Later in this series, we will show how to apply machine learning methods to derive a parametric EEG model. The model can be used to classify incoming EEG into attention or non-attention states, with a corresponding brain glyph/icon that intensifies and warns the user if attention has drifted or faded.

Authors:

Arjan S
Seidi Y

Contributors:

Aerton C
Tamara N