Common-mode Artifacts Research Articles

Analysis and Design of VCO-Based Neural Front-End With Mixed Domain Level-Crossing for Fast Artifact Recovery

Concurrent neural signal instrumentation withstanding neural stimulation artifacts is essential for bi-directional neural interfaces to guarantee signal integrity. In this work, different front-end structures and stimulation artifact mitigation techniques are firstly reviewed to benchmark their step response speed. Then, a mixed domain level-crossing scheme is proposed to achieve fast dynamic response with minimized hardware overhead. The benefit of extending the phase detection range of the phase detectors in VCO-based continuous time <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\rm \Delta \Sigma $ </tex-math></inline-formula> modulators is investigated with stability and noise consideration. Then a shift-register-based phase counter is proposed to extend the phase detectors’s detection range, thereby increase quantization resolution and stability margin for in-band noise optimization. The proposed VCO-based neural front-end was fabricated in a 180 nm CMOS process. The prototype achieves <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$6.38~\mu $ </tex-math></inline-formula> Vrms input-referred noise over 0.5 Hz-10 kHz bandwidth. With a linear input range of 120 mVpp, it exhibits a SNDR of 71.6 dB and a DR of 77.0 dB, which could be further extended up to 100 dB in the artifact adaption mode. Measurements verify that the proposed neural front-end can recover from rail-to-rail differential mode or common mode artifacts within 10 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{s}$ </tex-math></inline-formula> (minimum <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$6.25~\mu \text{s}$ </tex-math></inline-formula> ) while the superposed small signal can be recorded uninterruptedly.

Physiological Artifacts and the Implications for Brain-Machine-Interface Design.

The accurate measurement of brain activity by Brain-Machine-Interfaces (BMI) and closed-loop Deep Brain Stimulators (DBS) is one of the most important steps in communicating between the brain and subsequent processing blocks. In conventional chest-mounted systems, frequently used in DBS, a significant amount of artifact can be induced in the sensing interface, often as a common-mode signal applied between the case and the sensing electrodes. Attenuating this common-mode signal can be a serious challenge in these systems due to finite common-mode-rejection-ratio (CMRR) capability in the interface. Emerging BMI and DBS devices are being developed which can mount on the skull. Mounting the system on the cranial region can potentially suppress these induced physiological signals by limiting the artifact amplitude. In this study, we model the effect of artifacts by focusing on cardiac activity, using a current- source dipole model in a torso-shaped volume conductor. Performing finite element simulation with the different DBS architectures, we estimate the ECG common mode artifacts for several device architectures. Using this model helps define the overall requirements for the total system CMRR to maintain resolution of brain activity. The results of the simulations estimate that the cardiac artifacts for skull-mounted systems will have a significantly lower effect than non-cranial systems that include the pectoral region. It is expected that with a pectoral mounted device, a minimum of 60-80 dB CMRR is required to suppress the ECG artifact, depending on device placement relative to the cardiac dipole, while in cranially mounted devices, a 0 dB CMRR is sufficient, in the worst-case scenario. In addition, the model suggests existing commercial devices could optimize performance with a right-hand side placement. The methods used for estimating cardiac artifacts can be extended to other sources such as motion/muscle sources. The susceptibility of the device to artifacts has significant implications for the practical translation of closed-loop DBS and BMI, including the choice of biomarkers, the system design requirements, and the surgical placement of the device relative to artifact sources.

An 80-mVpp Linear-Input Range, 1.6-GΩ Input Impedance, Low-Power Chopper Amplifier for Closed-Loop Neural Recording That Is Tolerant to 650-mVpp Common-Mode Interference

Closed-loop neuromodulation is essential for the advance of neuroscience and for administering therapy in patients suffering from drug-resistant neurological conditions. Neural stimulation generates large differential and common-mode (CM) artifacts at the recording sites, which easily saturate traditional recording front ends. This paper presents a neural recording chopper amplifier capable of handling in-band 80-mVpp differential artifacts and 650-mVpp CM artifacts while preserving the accompanying small neural signals. New techniques have been proposed that introduce immunity to CM interference, increase the input impedance of the chopper amplifier to 1.6 $\text{G}\Omega $ , and increase the maximum realizable resistance of duty-cycled resistors (DCR) to 90 $\text{G}\Omega $ . These techniques enable our recording front-end to achieve a dynamic range of 74 dB (200 Hz–5 kHz) and 81 dB (1–200 Hz). Implemented in a 40-nm CMOS process, the prototype occupies an area of 0.069 mm2/channel, and consumes 2.8 $\mu \text{W}$ from a 1.2-V supply. The input-referred noise is 5.3 $\mu \text{V}_{\mathrm {\mathbf {rms}}}$ (200 Hz–5 kHz) and 1.8 $\mu \text{V}_{\mathrm {\mathbf {rms}}}$ (1–200Hz). The total harmonic distortion for a 40-mV $_{\mathrm {\mathbf {p}}}$ input at 1 kHz is −76 dB. This work improves the input impedance by 5.3 $\times $ for chopped front-ends, linear-input range by 2 $\times $ , maximum resistance of DCR by 32 $\times $ , and tolerance to CM interferers by 6.5 $\times $ , while maintaining comparable power and noise performance.

Method for high accuracy differential pressure measurements using fluid-filled catheters.

The advantage of measuring differential pressure using fluid-filled catheters is that the system is relatively inexpensive, but the readings are not accurate and affected by the common mode pressure (CMP) distortion. High accuracy differential pressure measurements are required in various biomedical applications, such as in fluid-dynamic test rigs, or in the cath-lab, from cardiac valves efficacy to functional assessment of arterial stenoses. We have designed and built a unique system in which the pressure difference was measured along the fluid flow inside a rigid circular tube using a fluid-filled double-lumen catheter. The differential pressure measurements were taken across two side-holes near the catheter distal tip, spaced apart by 3cm. The goal was to overcome the CMP error, which significantly distorted the output differential pressure signal and to formulate a restoration factor. A restoration formula was developed based on simultaneous gauge pressure measurements, and was tested in several different cases. Several representative cases are presented and show that the common mode artifact was reduced by factors of 12-27. The restored pressure gradient signal was validated using direct pressure drop measurements, and showed very good agreement.

Development of Gradient Descent Adaptive Algorithms to Remove Common Mode Artifact for Improvement of Cardiovascular Signal Quality

Common-mode noise degrades cardiovascular signal quality and diminishes measurement accuracy. Filtering to remove noise components in the frequency domain often distorts the signal. Two adaptive noise canceling (ANC) algorithms were tested to adjust weighted reference signals for optimal subtraction from a primary signal. Update of weight w was based upon the gradient term of the steepest descent equation: [see text], where the error epsilon is the difference between primary and weighted reference signals. nabla was estimated from Deltaepsilon(2) and Deltaw without using a variable Deltaw in the denominator which can cause instability. The Parallel Comparison (PC) algorithm computed Deltaepsilon(2) using fixed finite differences +/- Deltaw in parallel during each discrete time k. The ALOPEX algorithm computed Deltaepsilon(2)x Deltaw from time k to k + 1 to estimate nabla, with a random number added to account for Deltaepsilon(2) . Deltaw--> 0 near the optimal weighting. Using simulated data, both algorithms stably converged to the optimal weighting within 50-2000 discrete sample points k even with a SNR = 1:8 and weights which were initialized far from the optimal. Using a sharply pulsatile cardiac electrogram signal with added noise so that the SNR = 1:5, both algorithms exhibited stable convergence within 100 ms (100 sample points). Fourier spectral analysis revealed minimal distortion when comparing the signal without added noise to the ANC restored signal. ANC algorithms based upon difference calculations can rapidly and stably converge to the optimal weighting in simulated and real cardiovascular data. Signal quality is restored with minimal distortion, increasing the accuracy of biophysical measurement.