One-class Autoencoder Approach for Optimal Electrode Set-up Identification in Wearable EEG Event Monitoring

TL;DR

This paper presents a novel autoencoder-based method to identify the minimal and most effective electrode configurations for wearable EEG devices, enhancing comfort and performance in continuous health monitoring.

Contribution

It introduces a one-class autoencoder approach to optimize electrode placement in wearable EEG, reducing the number of electrodes needed for accurate event detection.

Findings

Optimal electrode set includes forehead and behind ear electrodes

Achieved an average F-score of 0.78 in alpha wave detection

Demonstrates learning-based optimization for wearable EEG design

Abstract

A limiting factor towards the wide routine use of wearables devices for continuous healthcare monitoring is their cumbersome and obtrusive nature. This is particularly true for electroencephalography (EEG) recordings, which require the placement of multiple electrodes in contact with the scalp. In this work, we propose to identify the optimal wearable EEG electrode set-up, in terms of minimal number of electrodes, comfortable location and performance, for EEG-based event detection and monitoring. By relying on the demonstrated power of autoencoder (AE) networks to learn latent representations from high-dimensional data, our proposed strategy trains an AE architecture in a one-class classification setup with different electrode set-ups as input data. The resulting models are assessed using the F-score and the best set-up is chosen according to the established optimal criteria. Using alpha wave detection as use case, we demonstrate that the proposed method allows to detect an alpha state from an optimal set-up consisting of electrodes in the forehead and behind the ear, with an average F-score of 0.78. Our results suggest that a learning-based approach can be used to enable the design and implementation of optimized wearable devices for real-life healthcare monitoring.