2013/11/15 by Zhilin Zhang, Bhaskar D. Rao, Zhang, Zhilin +3
Computer Science · Engineering · #Blind Source Separation Techniques #Cognitive Radio Networks and Spectrum Sensing #FOS: Computer and information sciences #Information Theory (cs.IT) #Machine Learning (stat.ML) #Wireless Body Area Networks
paper · pdf · doi:10.48550/arxiv.1311.3995
openalex publication_date 2013/11/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
As a lossy compression framework, compressed sensing has drawn much attention in wireless telemonitoring of biosignals due to its ability to reduce energy consumption and make possible the design of low-power devices. However, the non-sparseness of biosignals presents a major challenge to compressed sensing. This study proposes and evaluates a spatio-temporal sparse Bayesian learning algorithm, which has the desired ability to recover such non-sparse biosignals. It exploits both temporal correlation in each individual biosignal and inter-channel correlation among biosignals from different channels. The proposed algorithm was used for compressed sensing of multichannel electroencephalographic (EEG) signals for estimating vehicle drivers' drowsiness. Results showed that the drowsiness estimation was almost unaffected even if raw EEG signals (containing various artifacts) were compressed by 90%.