• Opamp-Less Sub-μW/Channel Δ-Modulated Neural-ADC With Super-GΩ Input Impedance
  • Authors: M Reza Pazhouhandeh, Hossein Kassiri, Aly Shoukry, Iliya Weisspapir, Peter L Carlen, Roman Genov
  • Publication date: 2020/12/14
  • Journal: IEEE Journal of Solid-State Circuits
  • Publisher: IEEE
  • Abstract:
    The presented 16-channel Δ-modulated neural analog-to-digital converter (ADC) exhibits tolerance to input dc offsets of any value, up to the supply voltage. It employs a dynamic differential-difference comparator architecture with a super-GΩ input impedance ensuring negligible gate-leakage current and well-matched differential inputs resulting in more than 78 dB of rejection of common-mode signals and artifacts. The all-digital nature of the presented Δ -ADC enables sampling of input signals at high oversampling ratios (OSRs) making the front-end immune to stimulation artifacts with differential amplitudes up to a limit that is scalable by the OSR (e.g., 10 mVPP at OSR = 10 k). Moreover, it allows the Δ-ADC to linearly scale down the power consumption required by the application’s recording bandwidth. The oversampled Δ-ADC achieves an effective number of bits (ENOB) of 9.7-bit and 2.6-μVRMS integrated input-referred noise over 1 Hz to 500-Hz bandwidth. It uses no large passives or statically biased circuits, such as operational amplifiers (Opamps) saving both channel area (0.011 mm²) and power consumption (0.99 μW). Experimentally measured results validate the key features of the design and include in vivo recordings in freely moving guinea pigs. The fabricated prototype system-on-a-chip (SoC) hosts an array of 16-channel neural-ADC with in-channel digitally programmable high-compliance current-mode biphasic stimulators as well as wireless circuitry for data communication and power/command reception.
2021-01-28T18:38:48+00:00journal, Paper|


  • Authors: Mansour Taghadosi, Hossein Kassiri
  • Publication date: 2020/11/6
  • Journal: IEEE Transactions on Circuits and Systems I: Regular Papers
  • Publisher: IEEE
  • Abstract:
    The development, analysis, and experimental validation of an energy storage algorithmic scheme for performance optimization of resonant inductive power receivers are presented. Motivated by the crucial role of efficient energy storage in the next generation of brain-implantable devices, we introduce an energy management strategy in the design of wireless powering links, in which, the key performance measure is the energy stored during a limited time interval rather than the average energy delivered to the load. The presented strategy is proven analytically to yield the theoretically-maximum energy storage efficiency over a pre-determined period of time. Additionally, thanks to the algorithm’s closed-form solution, the optimization can be done in real time, offering the potential for a solution that is adaptive to any variations in physical (e.g., coil separation, Rx rotation, etc.) and/or electrical (e.g., Q-factor, media conductivity, etc.) properties of the link, conditional to a low-power circuit implementation for its evaluation. The efficacy and precision of the solution obtained from the presented analytical model is confirmed with CAD-based simulation results, and later validated using experimental measurements. Our experimental results for two links with different characteristics (resonance frequency, coils size and separation, etc.) show a 52.5% and 67.5% improvement in overall energy storage efficiency compared to the standard CM receiver design in which resonance-to-charging switching is performed when the receiver’s LC tank energy accumulation starts to saturate. This is while the presented method does not require any calibration and is designed to be employed by any generic current-mode receiver.
2021-01-28T18:32:03+00:00journal, Paper|


  • Authors: Tayebeh Yousefi, Mansour Taghadosi, Alireza Dabbaghian, Ryan Siu, Gerd Grau, Georg Zoidl, Hossein Kassiri
  • Publication date: 2020/9/25
  • Journal: IEEE Transactions on Biomedical Circuits and Systems
  • Volume: 14
  • Issue: 6
  • Pages: 1274-1286
  • Publisher: IEEE
  • Abstract: This paper presents an energy-efficient mm-scale self-contained bidirectional optogenetic neuro-stimulator, which employs a novel highly-linear μ LED driving circuit architecture as well as inkjet-printed custom-designed optical μ lenses for light directivity enhancement. The proposed current-mode μ LED driver performs linear control of optical stimulation for the entire target range ( < 10 mA) while requiring the smallest reported headroom, yielding a significant boost in the energy conversion efficiency. A 30.46× improvement in the power delivery efficiency to the target tissue is achieved by employing a pair of printed optical μ lenses. The fabricated SoC also integrates two recording channels for LFP recording and digitization, as well as power management blocks. A micro-coil is also embedded on the chip to receive inductive power and our experimental results show a PTE of 2.24 % for the wireless link. The self-contained system including the μ LEDs, μ lenses and the capacitors required by the power management blocks is sized 6 mm 3 and weighs 12.5 mg. Full experimental measurement results for electrical and optical circuitry as well as in vitro measurement results are reported.
2021-01-28T18:26:12+00:00journal, Paper|

IEEE TBioCAS Dec 2019 – SVM

T. Zhan*, S. Fatmi*, S. Guraya*, H. Kassiri, “A Resource-Optimized VLSI Implementation of a Patient-Specific Seizure Detection Algorithm on a Custom-Made 2.2 cm2 Wireless Device for Ambulatory Epilepsy Diagnostics”, IEEE transactions on biomedical circuits and systems (TBioCAS), 2019, Early Access. (Invited, special issue on best papers of IEEE ISCAS’19 Conference)

2020-01-10T21:50:43+00:00journal, Paper|

IEEE TBioCAS Dec 2019 – Wearable EEG

A. Dabbaghian*, T. Yousefi*, S.Fatmi*, P. Shafia*, and Hossein Kassiri. “A 9.2-gram Fully-Flexible Wireless Ambulatory EEG Monitoring and Diagnostics Headband with Analog Motion Artifact Detection and Compensation.” IEEE transactions on biomedical circuits and systems (TBioCAS), 2019, Early Access. (Invited, special issue on best papers of IEEE ISCAS’19 Conference)

2020-01-10T21:50:57+00:00journal, Paper|

TBioCAS 2017

H. Kassiri, S. Tonekaboni, M. T. Salam, N. Soltani, K. Abdelhalim, J. L. Perez Velazquez, R. Genov, “Closed-Loop Neurostimulators: A Survey and a Seizure-Predicting Design Example for Intractable Epilepsy Treatment,” IEEE Transactions on Biomedical Circuits and Systems, Vol. 11, No. 5, pp. 1026-1040, Oct. 2017. (Invited, special issue on best papers of IEEE ISCAS’16 Conference) [Full Text

2019-01-02T22:47:20+00:00journal, Paper|

Epilepsia 2017

H. Kassiri, M. T. Salam, J. L. Perez Velazquez, R. Genov, “Brain Synchrony‐Contingent Neurostimulator for Treatment of Drug‐Resistant Epilepsy,” in “Seizure detection and neuromodulation: A summary of data presented at the XIII conference on new antiepileptic drug and devices (EILAT XIII),” edited by M. Bialer, et. al., Epilepsy Research, Vol. 130, pp. 34-36, Feb. 2017. [Full Text

2019-01-02T22:47:27+00:00journal, Paper|