An architecture for 1-bit localized compressive sensing with applications to EEG

Javier Haboba, Mauro Mangia, Riccardo Rovatti, Gianluca Setti

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Scopus citations


Compressed sensing exploits special signal features to extract its information content with a smaller amount of samples with respect to acquisition based on Nyquist theorem. While many theoretical results have proved the capabilities of this new paradigm, hardware implementations are still far from being practical. Here, we present a new architecture of analog to information converter that produces 1-bit compressive measurements. The performance of the architecture can be boosted if the signal to acquire features, beyond the classically required sparsity, also some sort of localization of its energy. The effectiveness of the architecture and of its enhancement is shown in the measurement of EEG, that presents a non-uniform spectral profile. © 2011 IEEE.
Original languageEnglish (US)
Title of host publication2011 IEEE Biomedical Circuits and Systems Conference, BioCAS 2011
Number of pages4
StatePublished - Dec 1 2011
Externally publishedYes


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