Compact low-power cortical recording architecture for compressive multichannel data acquisition

Shoaran, Mahsa; Kamal, Mahdad Hosseini; Pollo, Claudio; Vandergheynst, Pierre; Schmid, Alexandre (2014). Compact low-power cortical recording architecture for compressive multichannel data acquisition. IEEE transactions on biomedical circuits and systems, 8(6), pp. 857-870. Institute of Electrical and Electronics Engineers IEEE 10.1109/TBCAS.2014.2304582

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This paper introduces an area- and power-efficient approach for compressive recording of cortical signals used in an implantable system prior to transmission. Recent research on compressive sensing has shown promising results for sub-Nyquist sampling of sparse biological signals. Still, any large-scale implementation of this technique faces critical issues caused by the increased hardware intensity. The cost of implementing compressive sensing in a multichannel system in terms of area usage can be significantly higher than a conventional data acquisition system without compression. To tackle this issue, a new multichannel compressive sensing scheme which exploits the spatial sparsity of the signals recorded from the electrodes of the sensor array is proposed. The analysis shows that using this method, the power efficiency is preserved to a great extent while the area overhead is significantly reduced resulting in an improved power-area product. The proposed circuit architecture is implemented in a UMC 0.18 [Formula: see text]m CMOS technology. Extensive performance analysis and design optimization has been done resulting in a low-noise, compact and power-efficient implementation. The results of simulations and subsequent reconstructions show the possibility of recovering fourfold compressed intracranial EEG signals with an SNR as high as 21.8 dB, while consuming 10.5 [Formula: see text]W of power within an effective area of 250 [Formula: see text]m × 250 [Formula: see text]m per channel.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Department of Head Organs and Neurology (DKNS) > Clinic of Neurosurgery

UniBE Contributor:

Pollo, Claudio

Subjects:

600 Technology > 610 Medicine & health

ISSN:

1932-4545

Publisher:

Institute of Electrical and Electronics Engineers IEEE

Language:

English

Submitter:

Nicole Söll

Date Deposited:

16 Mar 2015 10:44

Last Modified:

12 Nov 2015 09:01

Publisher DOI:

10.1109/TBCAS.2014.2304582

PubMed ID:

24723633

BORIS DOI:

10.7892/boris.64546

URI:

https://boris.unibe.ch/id/eprint/64546

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