A limiting factor towards the wide real-life 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 setup , 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 setups 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 an alpha state from an optimal setup. The socalled wearable configuration, consisting of electrodes in the forehead and behind the ear, is the chosen optimal setup , 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 event related healthcare monitoring.