Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Impedance spectroscopy systems: Review and an all-digital adaptive IIR filtering approach
KTH, School of Electrical Engineering and Computer Science (EECS), Electronics, Integrated devices and circuits.ORCID iD: 0000-0002-9862-8255
KTH, School of Electrical Engineering and Computer Science (EECS), Electronics, Integrated devices and circuits.
KTH, School of Electrical Engineering and Computer Science (EECS), Electronics, Integrated devices and circuits.ORCID iD: 0000-0003-3802-7834
2017 (English)In: 2017 IEEE Biomedical Circuits and Systems Conference, Turin, October 19-21, 2017, Turin, Italy: Institute of Electrical and Electronics Engineers (IEEE), 2017Conference paper, Published paper (Refereed)
Abstract [en]

Impedance spectroscopy is a low-cost sensing technique that is generating considerable interest in wearable and implantable biomedical applications since it can be efficiently integrated on a single microchip. In this paper, the fundamental characteristics of the most well-known system architectures are presented, and a more robust and hardware-efficient solution is proposed. An all-digital implementation based on adaptive filtering is used for identifying the impedance parameters of a sample-under-test. The coefficients of an infinite-impulse-response (IIR) filter are tuned by an adaptive algorithm based on pseudo-linear regression and output-error formulation. A three-level pseudorandom noise generator with a concave power spectral density is employed without deteriorating the nominal performance. Proof-of-concept has been verified with behavioral simulations.

Place, publisher, year, edition, pages
Turin, Italy: Institute of Electrical and Electronics Engineers (IEEE), 2017.
Keywords [en]
Adaptive algorithms, Clocks, Frequency measurement, Impedance, Impedance measurement, Spectroscopy, Systems architecture
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electrical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-225867DOI: 10.1109/BIOCAS.2017.8325148Scopus ID: 2-s2.0-85050013856ISBN: 978-1-5090-5803-7 (electronic)OAI: oai:DiVA.org:kth-225867DiVA, id: diva2:1196678
Conference
2017 IEEE Biomedical Circuits and Systems Conference, BioCAS 2017, Politecnico di TorinoTorino, Italy, 19 October 2017 through 21 October 2017
Funder
Swedish Research Council
Note

QC 20180604

Available from: 2018-04-10 Created: 2018-04-10 Last updated: 2019-04-12Bibliographically approved
In thesis
1. Circuit Design Techniques for Implantable Closed-Loop Neural Interfaces
Open this publication in new window or tab >>Circuit Design Techniques for Implantable Closed-Loop Neural Interfaces
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Implantable neural interfaces are microelectronic systems, which have the potential to enable a wide range of applications, such as diagnosis and treatment of neurological disorders. These applications depend on neural interfaces to accurately record electrical activity from the surface of the brain, referred to as electrocorticography (ECoG), and provide controlled electrical stimulation as feedback. Since the electrical activity in the brain is caused by ionic currents in neurons, the bridge between living tissue and inorganic electronics is achieved via microelectrode arrays. The conversion of the ionic charge into freely moving electrons creates a built-in electrode potential that is several orders of magnitude larger than the ECoG signal, which increases the dynamic range, resolution, and power consumption requirements of neural interfaces. Also, the small surface area of microelectrodes implies a high-impedance contact, which can attenuate the ECoG signal. Moreover, the applied electrical stimulation can also interfere with the recording and ultimately cause irreversible damages to the electrodes or change their impedance. This thesis is devoted to resolving the challenges of high-resolution recording and monitoring the electrode impedance in implantable neural interfaces.

The first part of this thesis investigates the state-of-the-art neural interfaces for ECoG and identifies their limitations. As a result of the investigation, a high-resolution ADC is proposed and implemented based on a ΔΣ modulator. In order to enhance performance, dynamic biasing and area-efficient switched-capacitor circuits were proposed. The ΔΣ modulator is combined with the analog front-end to provide a complete readout solution for high-resolution ECoG recording. The corresponding chip prototype was fabricated in a 180 nm CMOS process, and the measurement results showed a 14-ENOB over a 300-Hz bandwidth while dissipating 54-μW.

The second part of this thesis expands upon the well-known methods for impedance measurements and proposes an alternative digital method for monitoring the electrode-tissue interface impedance. The proposed method is based on the system identification technique from adaptive digital filtering, and it is compatible with existing circuitry for neural stimulation. The method is simple to implement and performs wide-band measurements. The system identification was first verified through behavioral simulations and then tested with a board-level prototype in order to validate the functionality under real conditions. The measurement results showed successful identification of the electrode-electrolyte and electrode-skin impedance magnitudes.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2019. p. 72
Series
TRITA-EECS-AVL ; 2019:33
Keywords
Neural interface, ECoG, high-resolution, ADC, recording, delta-sigma modulator, system identification, impedance measurements
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Information and Communication Technology
Identifiers
urn:nbn:se:kth:diva-249435 (URN)978-91-7873-151-0 (ISBN)
Public defence
2019-05-17, Ka-Sal B (Sal Peter Weissglas), Kistagången 16,, Stockholm, 13:00 (English)
Opponent
Supervisors
Funder
Swedish Research CouncilSwedish Foundation for Strategic Research
Note

QC 20190412

Available from: 2019-04-12 Created: 2019-04-12 Last updated: 2019-04-12Bibliographically approved

Open Access in DiVA

fulltext(1074 kB)99 downloads
File information
File name FULLTEXT01.pdfFile size 1074 kBChecksum SHA-512
067bd1e25b7bf489496694a5dca7d684a3bcc2f8d3ce6c9f0933dce20a0a9088e902f9855858f021e68e48e266ae01012ea3b23a2efa5d03304cafa4e8ecbfd7
Type fulltextMimetype application/pdf

Other links

Publisher's full textScopushttps://ieeexplore.ieee.org/document/8325148/

Authority records BETA

Ivanisevic, NikolaRodriguez, SaulRusu, Ana

Search in DiVA

By author/editor
Ivanisevic, NikolaRodriguez, SaulRusu, Ana
By organisation
Integrated devices and circuits
Other Electrical Engineering, Electronic Engineering, Information Engineering

Search outside of DiVA

GoogleGoogle Scholar
Total: 99 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 266 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf