Real-time Reconstruction of EEG Signals from Compressive Measurements via Deep Learning

To elongate the battery life of sensors worn in wireless body area networks, recent studies have advocated compressing the acquired biological signals before transmitting them. The signals are compressed using compressive sensing (CS), by projecting them onto a lower dimension. The original signals are then recovered using CS recovery techniques at the base station, where the computational power is assumed to be abundant. This assumption however is not entirely true when a mobile phone acts as the base station. The computational capacity of a mobile phone is limited; therefore solving the CS recovery problem in the phone would be time consuming. In many cases (e,g. heart stroke detection or monitoring applications) this latency cannot be tolerated. In this work we propose a new technique to solve the inverse problem using stacked autoencoders. We show that the reconstruction of the proposed method can be done in real-time, and there is only a slight degradation in accuracy compared to CS based inversion methods.