One-class autoencoder approach for optimal electrode set identification in wearable EEG event monitoring.

A limiting factor towards the wide use of wearable devices for continuous healthcare monitoring is their cumbersome and obtrusive nature. This is particularly true in electroencephalography (EEG), where numerous electrodes are placed in contact with the scalp to perform brain activity recordings. In this work, we propose to identify the optimal wearable EEG electrode set, 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 combinations as input data. The model performance is assessed using the F-score. Alpha waves detection is the use case through which we demonstrate that the proposed method allows to detect a brain state from an optimal set of electrodes. The so-called wearable configuration, consisting of electrodes in the forehead and behind the ear, is the chosen optimal set, with an average F-score of 0.78. This study highlights the beneficial impact of a learning-based approach in the design of wearable devices for real-life event-related monitoring.