The choice of EEG reference has been widely studied. However, the choice of the most appropriate re-referencing for EEG data is still debated. Moreover, the role of EEG reference in the estimation of functional Brain-Heart Interplay (BHI), together with different multivariate modelling strategies, has not been investigated yet.
This study identifies the best methodology combining a proper EEG electrical reference and signal processing methods for an effective functional BHI assessment. The effects of the EEG reference among common average, mastoids average, Laplacian reference, Cz reference, and the reference electrode standardization technique (REST) were explored throughout different BHI methods including synthetic data generation (SDG) model, heartbeat-evoked potentials, heartbeat-evoked oscillations, and maximal information coefficient.
The SDG model exhibited high robustness between EEG references, whereas the maximal information coefficient method exhibited a high sensitivity. The common average and REST references for EEG showed a good consistency in the between-method comparisons. Laplacian, and Cz references significantly bias a BHI measurement.
The use of EEG reference based on a common average outperforms on the use of other references for consistency in estimating directed functional BHI. We do not recommend the use of EEG references based on analytical derivations as the experimental conditions may not meet the requirements of their optimal estimation, particularly in clinical settings.
The use of a common average for EEG electrical reference is concluded to be the most appropriate choice for a quantitative, functional BHI assessment.

To develop new concepts for minimum electric-field (E-field) gradient design, and to define the extents to which E-field can be reduced in gradient design while maintaining a desired imaging performance.
Efficient calculation of induced electric field in simplified patient models was integrated into gradient design software, allowing constraints to be placed on the peak E-field. Gradient coils confined to various build envelopes were designed with minimum E-fields subject to standard magnetic field constraints. We examined the characteristics of E-field-constrained gradients designed for imaging the head and body and the importance of asymmetry and concomitant fields in achieving these solutions.
For transverse gradients, symmetric solutions create high levels of E-fields in the shoulder region, while fully asymmetric solutions create high E-fields on the top of the head. Partially asymmetric solutions result in the lowest E-fields, balanced between shoulders and head and resulting in factors of 1.8 to 2.8 reduction in E-field for x-gradient and y-gradient coils, respectively, when compared with the symmetric designs of identical gradient distortion.
We introduce a generalized method for minimum E-field gradient design and define the theoretical limits of magnetic energy and peak E-field for gradient coils of arbitrary cylindrical geometry.

Enzymatic biofuel cell (EBFC)-based self-powered biochemical sensors obviate the need for external power sources thus enabling device miniaturization. While recent efforts driven by experimentalists illustrate the potential of EBFC-based sensors for real-time monitoring of physiologically relevant biochemicals, a robust mathematical model that quantifies the contributions of sensor components and empowers experimentalists to predict sensor performance is missing. In this paper, we provide an elegant yet simple equivalent circuit model that captures the complex, three-dimensional interplay among coupled catalytic redox reactions occurring in an EBFC-based sensor and predicts its output signal with high correlations to experimental observations. The model explains the trade-off among chemical design parameters such as the surface density of enzymes, various reaction constants as well as electrical parameters in the Butler-Volmer relationship. The model shows that the linear dynamic range and sensitivity of the EBFC-based sensor can be independently fine-tuned by changing the surface density of enzymes and electron mediators at the anode and by enhancing reductant concentrations at the cathode. The mathematical model derived in this work can be easily adapted to understand a wide range of two-electrode systems, including sensors, fuel cells, and energy storage devices.

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The electrical activity of cells is regulated by ion fluxes and chemical signaling between them is sustained by redox-reactive molecules. Consequently, current sensing represents a straightforward way to interface electronics with biology and a common detection tool in several applications spanning from patch-clamp and nanopores to micro-scale impedance tracking. Reaching pA resolution at the ms timescale represents a challenge for the readout circuit and here all the criticalities involved in the optimal design of the sensing electrode are reviewed. Advantages vs. drawbacks and risks of the use of silicon as active vs. passive substrate respectively are illustrated by means of experimental examples.

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