Type: Bachelor
Status: Assigned March 2023
Student: Mattias Formo

Federated learning (FL) is a state-of-the-art machine-learning technique developed by Google, where the users’ privacy is guaranteed by implementing one simple rule: “No personal data leaves the user-device”. This project will investigate FL techniques for cognitive load estimate. Cognitive load can be estimated through the analysis of from pupillometry data, brain activation data (EEG), breathing rate, heart rate, heart rate variability and other related physiological responses.

Specifically, this thesis has four main tasks:

overview excising datasets for cognitive load estimation;

develop a centralized machine learning pipeline for cognitive load estimation;

develop a FL pipeline for cognitive load estimation;

The main challenge of the work will be to develop enhanced privacy-aware models for cognitive-load modeling using federated learning.

For more information contact: Martin Gjoreski