IEEE CICC 2021

  • A 24-Channel Neurostimulator IC with One-Shot Impedance-Adaptive Channel-Specific Charge Balancing
  • Authors: Fatemeh Eshaghi, Esmaeil Najafiaghdam, Hossein Kassiri
  • Conference date: April 25-30, 2021
  • Conference: IEEE Custom Integrated Circuits Conference
  • Abstract:Thanks to their precise control of spatial (µC/cm2) and temporal (nC/ms) density of charge injection to the living tissue, current-mode drivers have been the most popular front-end circuit for conducting safe and charge-neutral neuro-stimulation of the brain. However, due to the lack of control on their high-impedance output node’s voltage, these drivers are vulnerable to residual charge accumulation caused by anodic-cathodic mismatch. Even with no systemic mismatch, imbalanced electrochemical reactions (oxidation and reduction) at the electrode-tissue interface could lead to charge accumulation, which in long term, causes electrode corrosion and neural damage [1]. Several passive and active methods have been proposed in the literature to address this issue (Fig. 1(a)). However, their performance is limited due to being either non-scalable (off-chip blocking capacitors), causing unintended stimulation (shorting, high-amplitude pulse insertion), or imposing significant time constraints on the rest period (current-controlled shorting, low-intensity pulse insertion, gradual phase control), hence, limiting the maximum frequency of stimulation. In this work, we present a 24-channel neurostimulator with a charge balancing technique that imposes no timing limitation, offers programmable tolerance to residual charge accumulation, and is safe to unintended stimulation.
2021-01-28T18:52:52+00:00conference, Paper|

IEEE CICC 2021

  • An Analog Low-Power Highly-Accurate Link-Adaptive Energy Storage Efficiency Maximizer for Resonant CM Wireless Power Receivers
  • Authors: Mansour Taghadosi and Hossein Kassiri
  • Conference date: April 25-30, 2021
  • Conference: IEEE Custom Integrated Circuits Conference
  • Abstract: The power del1ivered wirelessly to implantable neural interfaces supplies two categories of loads with distinct consumption patterns: small-and-continuous (e.g., recording circuits, signal conditioning) or large-and-intermittent (e.g., electrical stimulation, wireless transmission). For the weakly-coupled mm-scale implants, the induced power level at the receiver coil (Rx) is typically far below the required instantaneous power of the large-intermittent loads [1]. Therefore, these loads could only be supplied through storage of excess incoming energy during their off cycles. As such, the energy storage efficiency determines how often and how powerful a high-power event (e.g., data transmission, stimulation) could take place. Motivated by this, a variety of circuit ideas for energy delivery optimization are reported, mostly focused on current-mode (CM) receivers, mainly due to their superior performance in weakly-coupled links (compared to voltage-mode receivers). However, the optimization is either done only for resistive loads (i.e., not optimizing storage efficiency) [2-3], or done pre-operation (i.e., offline), hence, not adaptive to link variations (e.g., implant movements, media changes, etc.) [4-5]. We present a low-power integrated circuit (IC) that senses the peak voltage at the Rx coil (VRx(peak)), calculates the optimal timing scheme for maximum energy storage efficiency in real time, and operates the CM receiver accordingly. This closed-loop scheme makes the presented work adaptive to any link variation and needless of calibration.
2021-01-28T18:47:58+00:00conference, Paper|

IEEE EMBC 2020

Authors: Tayebeh Yousefi, Alireza Dabbaghian, Hossein Kassiri
Publication date: 2020/7/20
Source: 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
Pages: 4479-4482
Publisher: IEEE

Abstract:

Motion artifacts are arguably the most important issue in the development of wearable ambulatory EEG devices. Designing circuits and systems capable of high-quality EEG recording regardless of these artifacts requires a clear understanding of how the electrode-skin interface is affected by physical motions. In this work, first, we report statistically-significant experimental characterization results of electrode-skin interface impedance for dry contact and non-contact electrodes in the presence of various motions. This leads to a model describing the motion-induced electrode-skin interface impedance variations for these electrodes. Next, a critical review of the possible analog front-end circuits for surface EEG recording is presented, followed by theoretical circuit analysis discussing the effect of electrode movements on the operation of these circuits. Inspired by the developed model and the analytical review, a novel front-end architecture capable of extracting motion from the EEG signal during the amplification stage is presented and experimentally characterized.
2021-01-28T18:25:57+00:00conference, Paper|