Efficient Compressed Sensing for Wireless Neural Recording: A Deep Learning Approach

Abstract

Data compression is crucial for resource-constrained wireless neural recording applications with limited data bandwidth, and compressed sensing (CS) theory has successfully demonstrated its potential in neural recording applications. Based on deep learning theory, this paper presents a binarized autoencoder scheme for CS, in which a binary sensing matrix and a noniterative recovery solver are jointly optimized. Experimental results on synthetic dataset reveal that the proposed approach outperforms the state-of-the-art CS-based methods both in terms of recovery quality and computation time.