Distributed signal processing algorithms for auditory attention detection with chronic EEG sensor networks

From 01-10-2016 to 30-09-2021

Description

Electroencephalography (EEG) is a non-invasive neuromonitoring technique, with potential use for chronic daily-life monitoring of brain activity. A variety of miniature EEG devices have been proposed for chronic use, but these typically measure only a few EEG channels over a highly localized area, which is insufficient for many potential chronic-EEG applications. In this project, we aim to overcome this problem by deploying a multitude of such miniature EEG modules onto the scalp, and let them communicate over wireless links in a sensor network-like architecture. To reduce energy consumption to viable levels, we will develop distributed EEG signal processing algorithms where the EEG signals are processed locally at each module, while exchanging compressed data with the other modules in their close neighborhood. The algorithm design will focus on EEG-based auditory attention detection, which has applications in future neuro-steered hearing prostheses, and which serves as a use case throughout the project.

Team

• Sabine Van Huffel, Co-promoter
• Alexander Bertrand, Promoter
• Abhijith Mundanad Narayanan, Team member

Financing

Funding: KU Leuven - Internal Funding KU Leuven

Program/Grant Type: C1 - KU Leuven Category 1 Funding