Spectral Channels Research Articles

Long-wave infrared multi-spectral filter arrays based on surface plasma polaritons

An infrared multi-spectral filter based on surface plasmon polaritons is proposed in this study. It is used to achieve optical filtering in the long-wave infrared range of 8-14μm. After coupling with the plasma polaritons excited on the surface of Au film, the incident light propagates through periodic sub-wavelength through-holes. We analyzed the transmission characteristics of the filter with periodic sub-wavelength through-holes and its local field distribution using the Finite-Difference Time-Domain (FDTD), and carried out the tolerance analysis to demonstrate the possibility of large-scale and low-cost processing. The simulation results indicated that our proposed filter attained four spectral channels in the spectral range of 8 to 14 μm with peak energy transmittance up to about 60%, and wide tolerances as well as polarization-insensitive characteristics. Our proposed long-wave infrared filters can provide efficient and low-cost solutions for lightweight multi-spectral devices and all-weather detection.

SpectraTrack: megapixel, hundred-fps, and thousand-channel hyperspectral imaging

Hyperspectral imaging (HSI) finds broad applications in various fields due to its substantial optical signatures for the intrinsic identification of physical and chemical characteristics. However, it faces the inherent challenge of balancing spatial, temporal, and spectral resolution due to limited bandwidth. Here we present SpectraTrack, a computational HSI scheme that simultaneously achieves high spatial, temporal, and spectral resolution in the visible-to-near-infrared (VIS-NIR) spectral range. We deeply investigated the spatio-temporal-spectral multiplexing principle inherent in HSI videos. Based on this theoretical foundation, the SpectraTrack system uses two cameras including a line-scan imaging spectrometer for temporal-multiplexed hyperspectral data and an auxiliary RGB camera to capture motion flow. The motion flow guides hyperspectral reconstruction by reintegrating the scanned spectra into a 4D video. The SpectraTrack system can achieve around megapixel HSI at 100 fps with 1200 spectral channels, demonstrating its great application potential from drone-based anti-vibration video-rate HSI to high-throughput non-cooperative anti-spoofing.

HulaCCR1, a pump-like cation channelrhodopsin discovered in a lake microbiome

Channelrhodopsins are light-gated ion channels consisting of seven transmembrane helices and a retinal chromophore, which are used as popular optogenetic tools for modulating neuronal activity. Cation channelrhodopsins (CCRs), first recognized as the photoreceptors in the chlorophyte Chlamydomonas reinhardtii, have since been identified in diverse species of green algae, as well in other unicellular eukaryotes. The CCRs from non-chlorophyte species are commonly referred to as bacteriorhodopsin-like cation channelrhodopsins, or BCCRs, as most of them feature the three characteristic amino acid residues of the “DTD motif” in the third transmembrane helix (TM3 or helix C) matching the canonical DTD motif of the well-studied archaeal light-driven proton pump bacteriorhodopsin. Here, we report characterization of HulaCCR1, a novel BCCR identified through metatranscriptomic analysis of a unicellular eukaryotic community in Lake Hula, Israel. Interestingly, HulaCCR1 has an ETD motif in which the first residue of the canonical motif is substituted for glutamate. Electrophysiological measurements of the wild-type and a mutant with a DTD motif of HulaCCR1 suggest the critical role of the first glutamate in spectral tuning and channel gating. Additionally, HulaCCR1 exhibits long extensions at the N- and C-termini. Photocurrents recorded from a truncated variant without the signal peptide predicted at the N-terminus were diminished, and membrane localization of the truncated variant significantly decreased, indicating that the signal peptide is important for membrane trafficking of HulaCCR1. These characteristics of HulaCCR1 would be related to a new biological significance in the original unidentified species, distinct from those known for other BCCRs.

Detection of morphometric indicators of the soil surface of a grape plantation using spectral bands of satellite images

Introduction. Soils play an important role in the approximately 30-year period of operation of a grape planting, influencing plant growth, their yield and the quality of the grapes. In this study, the morphometric parameters of the surface soil layer of a grape plantation were studied using spectral channels of satellite images.Methodology. The methodology included the use of a “random forest” algorithm to classify soil cover using spectral channels and normalized satellite image indices and analyze the main physicochemical properties of soils. Accuracy was assessed using RMSD and confidence intervals calculated via bootstrapping.Results. The study revealed significant differences in the spectral reflectivity of different site options, which was due to carbonate content, humidity levels and the amount of humus. Areas with high carbonate and moisture content showed higher standard deviation values in the spectral channels. Studying the spectral characteristics of the soil surface makes it possible to effectively classify different areas based on remote sensing data. Analysis of combinations of spectral channels revealed an optimal set of three channels (B12, B11, B8A) with a minimum standard deviation when classifying an image into six soil variants of areas. For classification, a composition of five normalized indices can also be used, but in this case the calculation time increases significantly with a larger standard deviation and a larger confidence interval range. Using machine learning, six distinct soil surface types were segmented, demonstrating the complexity of the field›s soil mosaic. These results are critical for improving vineyard management and productivity

Monitoring of Heracleum sosnowskyi Manden Using UAV Multisensors: Case Study in Moscow Region, Russia

Detection and mapping of Sosnowsky’s hogweed (HS) using remote sensing data have proven effective, yet challenges remain in identifying, localizing, and eliminating HS in urban districts and regions. Reliable data on HS growth areas are essential for monitoring, eradication, and control measures. Satellite data alone are insufficient for mapping the dynamics of HS distribution. Unmanned aerial vehicles (UAVs) with high-resolution spatial data offer a promising solution for HS detection and mapping. This study aimed to develop a method for detecting and mapping HS growth areas using a proposed algorithm for thematic processing of multispectral aerial imagery data. Multispectral data were collected using a DJI Matrice 200 v2 UAV (Dajiang Innovation Technology Co., Shenzhen, China) and a MicaSense Altum multispectral camera (MicaSense Inc., Seattle, WA, USA). Between 2020 and 2022, 146 sites in the Moscow region of the Russian Federation, covering 304,631 hectares, were monitored. Digital maps of all sites were created, including 19 digital maps (orthophoto, 5 spectral maps, and 13 vegetation indices) for four experimental sites. The collected samples included 1080 points categorized into HS, grass cover, and trees. Student’s t-test showed significant differences in vegetation indices between HS, grass, and trees. A method was developed to determine and map HS-growing areas using the selected vegetation indices NDVI > 0.3, MCARI > 0.76, user index BS1 > 0.10, and spectral channel green > 0.14. This algorithm detected HS in an area of 146.664 hectares. This method can be used to monitor and map the dynamics of HS distribution in the central region of the Russian Federation and to plan the required volume of pesticides for its eradication.

Hyperspectral Image Classification Algorithm for Forest Analysis Based on a Group-Sensitive Selective Perceptual Transformer

Substantial advancements have been achieved in hyperspectral image (HSI) classification through contemporary deep learning techniques. Nevertheless, the incorporation of an excessive number of irrelevant tokens in large-scale remote sensing data results in inefficient long-range modeling. To overcome this hurdle, this study introduces the Group-Sensitive Selective Perception Transformer (GSAT) framework, which builds upon the Vision Transformer (ViT) to enhance HSI classification outcomes. The innovation of the GSAT architecture is primarily evident in several key aspects. Firstly, the GSAT incorporates a Group-Sensitive Pixel Group Mapping (PGM) module, which organizes pixels into distinct groups. This allows the global self-attention mechanism to function within these groupings, effectively capturing local interdependencies within spectral channels. This grouping tactic not only boosts the model’s spatial awareness but also lessens computational complexity, enhancing overall efficiency. Secondly, the GSAT addresses the detrimental effects of superfluous tokens on model efficacy by introducing the Sensitivity Selection Framework (SSF) module. This module selectively identifies the most pertinent tokens for classification purposes, thereby minimizing distractions from extraneous information and bolstering the model’s representational strength. Furthermore, the SSF refines local representation through multi-scale feature selection, enabling the model to more effectively encapsulate feature data across various scales. Additionally, the GSAT architecture adeptly represents both global and local features of HSI data by merging global self-attention with local feature extraction. This integration strategy not only elevates classification precision but also enhances the model’s versatility in navigating complex scenes, particularly in urban mapping scenarios where it significantly outclasses previous deep learning methods. The advent of the GSAT architecture not only rectifies the inefficiencies of traditional deep learning approaches in processing extensive remote sensing imagery but also markededly enhances the performance of HSI classification tasks through the deployment of group-sensitive and selective perception mechanisms. It presents a novel viewpoint within the domain of hyperspectral image classification and is poised to propel further advancements in the field. Empirical testing on six standard HSI datasets confirms the superior performance of the proposed GSAT method in HSI classification, especially within urban mapping contexts, where it exceeds the capabilities of prior deep learning techniques. In essence, the GSAT architecture markedly refines HSI classification by pioneering group-sensitive pixel group mapping and selective perception mechanisms, heralding a significant breakthrough in hyperspectral image processing.

A graph-guided transformer based on dual-stream perception for hyperspectral image classification

ABSTRACT The excellent capabilities of Transformers and Graph Neural Networks (GNNs) in modelling long-range dependencies and handling irregular data have led to their widespread application in hyperspectral image (HSI) classification tasks. However, the Graph Transformer combining both advantages is rarely used in this field and has some limitations. Current Graph Transformers consider interactions between all nodes within the graph, adding complexity and introducing unnecessary information from noisy nodes. Moreover, the rich spectral information in HSIs is often ignored, and there is a lack of effective fusion of spatial information. In this paper, we propose a dual-stream graph-guided Transformer for HSI classification. In spatial dimension, superpixels are utilized to guide spatial graph generation, capturing global topological dependencies and local details effectively in HSIs. In terms of spectrum, we innovatively construct a spectral graph based on spectral channels and adopt a contribution score-based strategy to adaptively filter out irrelevant edges, achieving sparsity while preserving spectral context relationships. Experimental results demonstrate the significant competitive advantage of our method in HSI classification tasks on three public datasets. The code is available at https://github.com/youngboy03/GTDPNet.

Design of geostationary orbit ultrawide FOV long-wave infrared imaging spectrometer

The geostationary orbit imaging spectrometer offers distinct advantages for diverse applications in remote sensing. To enhance swath and data richness, there is a growing trend toward spectrometers with broader fields of view (FOV) and extended slit lengths. Optical systems equipped with these features face significant challenges in aberration correction. This paper investigates the parameters of geostationary orbits and designs a spectroscopic system capable of achieving ultrawide FOV without the need for stitching spectrograph subsystems. Additionally, it also designs a front-end telescope system that matches the spectroscopic system. The spectroscopic system features a slit length of 240 mm, employing an enhanced Offner structure for a long slit spectroscopic system, incorporating a freeform surface on the third mirror. To inhibit the effects of radiation stray light, the system's exit pupil is controlled to be positioned in front of the image plane to facilitate matching with the cold shield. The analysis indicates that the spectral resolution of the long-wave infrared imaging spectrometer system is 50 nm, with a total of 90 spectral sampling channels. The system's spatial resolution is 100 m, enabling a swath width of 400 km. Furthermore, the modulation transfer function (MTF) exceeds 0.42 at the Nyquist frequency of 8.35 lp/mm. The maximum RMS radius of the system is less than 27 μm at wavelengths ranging from 8 to 12.5 μm.

Spectral channels increase of multi-wavelength visible laser enabled by SHG-SFG hybrid processes

Spectral evolution in nonlinear optical processes, such as second harmonic generation (SHG) and sum frequency generation (SFG), plays a crucial role in multi-wavelength generation through nonlinear frequency conversion. In this study, the enhancement of spectral performance in a multi-wavelength visible laser facilitated by SHG-SFG hybrid processes is proposed and demonstrated, for the first time to our knowledge. An output of up to eleven wavelengths can be achieved using a six-wavelength pump. Theoretical analysis suggests that the increase in spectral channel count is attributed to the SHG-SFG hybrid processes. Additionally, each nonlinear process operates independently without competition under small-signal approximation, validated through temperature variations. This research not only elucidates the mechanism of spectral evolution in hybrid nonlinear processes but also presents a viable method for improving the spectral performance of a multi-wavelength visible light source.

Optimizing ExoMars Rover Remote Sensing Multispectral Science II: Choosing and Using Multispectral Filters for Dynamic Planetary Surface Exploration With Linear Discriminant Analysis

AbstractIn this paper we address two problems associated with data‐limited dynamic spacecraft exploration: data‐prioritization for transmission, and data‐reduction for interpretation, in the context of ESA ExoMars rover multispectral imaging. We present and explore a strategy for selecting and combining subsets of spectral channels captured from the ExoMars Panoramic Camera, and attempt to seek hematite against a background of phyllosilicates and basalts as a test case scenario, anticipated from orbital studies of the rover landing site. We compute all available dimension reductions on the material reflectance spectra afforded by 4 spectral parameter types, and consider all possible paired combinations of these. We then find the optimal linear combination of each pair whilst evaluating the resultant target‐vs.‐background separation in terms of the Fisher Ratio and classification accuracy, using Linear Discriminant Analysis. We find ∼50,000 spectral parameter combinations with a classification accuracy >95% that use 6‐or‐less filters, and that the highest accuracy score is 99.6% using 6 filters, but that an accuracy of >99% can still be achieved with 2 filters. We find that when the more computationally efficient Fisher Ratio is used to rank the combinations, the highest accuracy is 99.1% using 4 filters, and 95.1% when limited to 2 filters. These findings are applicable to the task of time‐constrained planning of multispectral observations, and to the evaluation and cross‐comparison of multispectral imaging systems at specific material discrimination tasks.

High-throughput single cell analysis using multi-focus Raman spectroscopy under randomly interleaved scattering projection

Raman microspectroscopy is a powerful tool for label-free monitoring of single-cell dynamics. However, the traditional single-point acquisition mode is extremely inefficient because it only analyzes one cell at a time. We propose a method that combines multi-focus Raman excitation with random interleaving of scattering projections. The approach uses a time-sharing multi-focus array to simultaneously excite multiple biological cells and synchronously projects their Raman spectra into a single spectral channel with varying shifts. The spectral shifts of the cells are randomly interleaved during data acquisition, resulting in a sequence of mixed spectra from which a compressive sensing method is utilized to reconstruct the time-lapse Raman spectra of the individual cells. The method’s feasibility and performance are validated by numerical modeling and experimental investigations into biological spore germination. The results indicated that the throughput of single cell analysis can be increased by up to a factor of 15. The reconstructed spectra of the individual cells demonstrated exceptional fidelity, faithfully capturing the cellular changes in the individual cells. The developed technology paves the way towards high-throughput, label-free monitoring of living single cells, which has promising applications in cell biology.

Miniaturized Hyperspectral Imager Utilizing a Reconfigurable Filter Array for Both High Spatial and Spectral Resolutions.

Miniaturized hyperspectral imaging based on filter arrays has attracted much attention in consumer applications, such as food safety and biomedical applications. In this Letter, we demonstrate a miniaturized hyperspectral imager using a reconfigurable filter array to tackle the existing trade-off issue between the spectral and spatial resolutions. Utilizing tens of intermediate states of a vanadium dioxide cavity, we increase the total number of physical spectral channels by tens of times from a 2 × 2 mosaic filter unit, providing both high spatial and spectral resolutions for spectral imaging. The reconfigurable filter has a good spectral resolvability of 10 nm in the visible range with a wavelength inaccuracy of less than 2.1 nm. Hyperspectral imaging is demonstrated with a frame rate of 4.5 Hz.

Electrically tunable infrared optics enabled by flexible ion-permeable conducting polymer-cellulose paper

Materials that provide dynamically tunable infrared (IR) response are important for many applications, including active camouflage and thermal management. However, current IR-tunable systems often exhibit limitations in mechanical properties or practicality of their tuning modalities, or require complex and costly fabrication methods. An additional challenge relates to providing compatibility between different spectral channels, such as allowing an object to be reversibly concealed in the IR without making it appear in the visible range. Here, we demonstrate that conducting polymer-cellulose papers, fabricated through a simple and cheap approach, can overcome such challenges. The papers exhibit IR properties that can be electrochemically tuned with large modulation (absolute emissivity modulation of 0.4) while maintaining largely constant response in the visible range. Owing to high ionic and electrical conductivity, the tuning of the top surface can be performed electrochemically from the other side of the paper even at tens of micrometer thicknesses, removing the need for overlaying electrode and electrolyte in the optical beam path. These features enabled a series of electrically tunable IR devices, where we focus on demonstrating dynamic radiative coolers, thermal camouflage, anti-counterfeiting tags, and grayscale IR displays. The conducting polymer-cellulose papers are sustainable, cheap, flexible and mechanically robust, providing a versatile materials platform for active and adaptive IR optoelectronic devices.

Enhancing Thomson scattering polychromator performance with multi-pass spectral filters.

In photon-deficient, noncollective Thomson scattering diagnostics, filter polychromators are typically employed in the spectral analysis of Thomson-scattered signals to achieve acceptable signal-to-noise performance. Currently, the most common polychromator filter configuration employs a set of single-passband optical filters that define individual spectral channels. Here, we introduce a new spectral analysis method for Thomson scattering based on spectral filters with multiple passbands, referred to as Thomson scattering spectral multiplexing. Implementing multi-bandpass spectral filters on polychromators increases the achievable range of electron temperature measurement for a given number of filters employed. In addition, Thomson scattering spectral multiplexing reduces systematic measurement uncertainty, with fewer required spectral channels, thereby decreasing light loss from reduced optical element interactions. A multi-bandpass filter set, optimized by a genetic algorithm, has been successfully installed and tested on the Helically Symmetric eXperiment (HSX), demonstrating the benefits of the Thomson scattering spectral multiplexing method.

Snapshot Multi-Wavelength Birefringence Imaging.

A snapshot multi-wavelength birefringence imaging measurement method was proposed in this study. The RGB-LEDs at wavelengths 463 nm, 533 nm, and 629 nm were illuminated with circularly polarized light after passing through a circular polarizer. The transmitted light through the birefringent sample was captured by a color polarization camera. A single imaging process captured light intensity in four polarization directions (0°, 45°, 90°, and 135°) for each of the three RGB spectral wavelength channels, and subsequently measured the first three elements of Stokes vectors (S0, S1, and S2) after the sample. The birefringence retardance and fast-axis azimuthal angle were determined simultaneously. An experimental setup was constructed, and polarization response matrices were calibrated for each spectral wavelength channel to ensure the accurate detection of Stokes vectors. A polymer true zero-order quarter-wave plate was employed to validate measurement accuracy and repeatability. Additionally, stress-induced birefringence in a PMMA arch-shaped workpiece was measured both before and after the application of force. Experimental results revealed that the repeatability of birefringence retardance and fast-axis azimuthal angle was better than 0.67 nm and 0.08°, respectively. This approach enables multispectral wavelength, high-speed, high-precision, and high-repeatability birefringence imaging measurements through a single imaging session.

Stray light analysis and suppression of a UV multiple sub-pupil ultra-spectral imager

A multiple sub-pupil ultra-spectral imager system for simultaneous detection of multiple substances is proposed. By employing pupil separation prisms and grating multilevel spectra, the system achieves simultaneous detection of three spectral channels with a single spectrometer and detector, featuring an ultra-high spectral resolution of 0.1 nm. However, due to simultaneous detection of three channels, the system suffers from significant stray light issues. In response to this problem, a UV multiple sub-pupil ultra-spectral imager system optical-machinery model is constructed in this article. The main sources of stray light are determined through theoretical derivation and simulation analysis, and the level of stray light in the system is analyzed using simulation analysis software. A structure for stray light suppression is optimized, and the use of multispectral filters is proposed to suppress stray light generated by spectral crosstalk in the system. The effectiveness of stray light suppression is evaluated based on the energy response of the receiving surface. The analysis results show that after optimization, the level of stray light in the T1 channel is reduced from 1.4% to 0.65‰, in the T2 channel from 3.0‰ to 0.5‰, and in the T3 channel from 0.16‰ to 0.05‰. Therefore, it can be concluded that the proposed method for stray light suppression in the UV multiple sub-pupil ultra-spectral imager system addressed in this article meets the indicator requirements.

Multiplexed In Vivo Imaging with Fluorescence Lifetime-Modulating Tags.

Fluorescence lifetime imaging microscopy (FLIM) opens new dimensions for highly multiplexed imaging in live cells and organisms using differences in fluorescence lifetime to distinguish spectrally identical fluorescent probes. Here, a set of fluorescence-activating and absorption-shifting tags (FASTs) capable of modulating the fluorescence lifetime of embedded fluorogenic 4-hydroxybenzylidene rhodanine (HBR) derivatives is described. It is shown that changes in the FAST protein sequence can vary the local environment of the chromophore and lead to significant changes in fluorescence lifetime. These fluorescence lifetime-modulating tags enable multiplexed imaging of up to three targets in one spectral channel using a single HBR derivative in live cells and live zebrafish larvae. The combination of fluorescence lifetime multiplexing with spectral multiplexing allows to successfully image six targets in live cells, opening great prospects for multicolor fluorescence lifetime multiplexing.

Spatially resolved dual-comb sensing with a single electro-optic modulator

We demonstrate spatially resolved sensing by a novel approach that combines an infrared camera and a simplified dual-comb illumination arrangement. Specifically, our scheme employs a continuous-wave laser and only one electro-optic modulator to simultaneously create a pair of mutually coherent optical frequency combs, each one with a slightly different line spacing. The system operates by measuring this dual-comb spectrum from a sequence of acquired images, in order to recover the spectral response of every spatial point of a sample. Thanks to its excellent stability, our approach ensures integration times well in excess of 10 s. The result is an utterly simple multi-spectral imager, capable of resolving up to 127 independent comb lines (spectral channels) across 16 k individual spatial positions, with a digitization sampling rate close to 1 kHz. As a proof of concept, we measure spatial variations of the refractive index of a low-reflectivity etalon undergoing a heating process. This sensing unit has a great potential to work as a field-deployable system for the determination of the spatial distribution of external perturbations such as temperature or strain. We comprehensively discuss the current advantages and limitations of our sensing approach, as well as its further developments.

The MIRI/MRS Library. I. Empirically correcting detector charge migration in unresolved sources

The James Webb Space Telescope (JWST) has been collecting scientific data for over two years now. The Medium Resolution Spectrometer (MRS) of the Mid-InfraRed Instrument (MIRI) has been one of the telescope's most popular modes, and has already produced ground-breaking results. Scientists are now looking deeper into the data for new exciting discoveries, which introduces the need to characterise and correct known systematic effects to reach the photon noise limit. Five important limiting factors for the MRS are the pointing accuracy, non-linearity, detector charge migration, detector scattering---resulting in both spatial broadening and spectral interferometric fringing---the accuracy of the point-spread function (PSF) model, and the complex interplay between these. The Cycle 2 calibration programme 3779, entitled ‘The MIRI/MRS Library', proposed a 72-point intra-pixel dither raster of the calibration star 10-Lac, which provides a unique dataset tailored for the purpose of addressing the limiting factors on the MRS data accuracy. In this first work of the paper series, we aim to address the degeneracy between the non-linearity and charge migration (brighter-fatter effect) that affect the pixel voltage integration ramps of the MRS. Due to the low flux in the longer wavelengths, we only do this in the 4.9 to 11.7 micron region (spectral channels 1 and 2). We fitted the ramps individually per pixel and dither, in order to fold in the deviations from classical non-linearity that are caused by charge migration. The ramp shapes should be repeatable depending on the part of the PSF that is sampled. By doing so, we defined both a grid-based linearity correction, and an interpolated linearity correction. Including the change in ramp shape due to charge migration yields significant improvements compared to the uniform illumination assumption that is currently used by the standard JWST calibration pipeline. The standard deviation on the pixel ramp residual non-linearity is between 70-90<!PCT!> smaller than the current standard pipeline when self-calibrating with the grid. We are able to interpolate these coefficients to apply to any unresolved source not on the grid points, resulting in an up to 70<!PCT!> smaller standard deviation on the residual deviation from linearity. After applying the correction, the full-width at half maximum is up to 20<!PCT!> narrower for sources that cover the full pixel dynamic range. Furthermore, the depth of the fringes is now consistent up the ramp, improving the standard deviation on the difference in fringe depth between the start and ends of integrations by sim 60<!PCT!>. Pointing-specific linearity corrections allow us to accurately model the pixel ramps across the PSF, and for the first time, fix the systematic deviation in the slopes. In this work we demonstrated this for unresolved sources. The discovered trends with PSF sampling suggest that, in the future, we may be able to model ramps for spatially extended and resolved illumination as well.

Decreasing the physical gap in the neural-electrode interface and related concepts to improve cochlear implant performance.

Cochlear implants (CI) represent incredible devices that restore hearing perception for those with moderate to profound sensorineural hearing loss. However, the ability of a CI to restore complex auditory function is limited by the number of perceptually independent spectral channels provided. A major contributor to this limitation is the physical gap between the CI electrodes and the target spiral ganglion neurons (SGNs). In order for CI electrodes to stimulate SGNs more precisely, and thus better approximate natural hearing, new methodologies need to be developed to decrease this gap, (i.e., transitioning CIs from a far-field to near-field device). In this review, strategies aimed at improving the neural-electrode interface are discussed in terms of the magnitude of impact they could have and the work needed to implement them. Ongoing research suggests current clinical efforts to limit the CI-related immune response holds great potential for improving device performance. This could eradicate the dense, fibrous capsule surrounding the electrode and enhance preservation of natural cochlear architecture, including SGNs. In the long term, however, optimized future devices will likely need to induce and guide the outgrowth of the peripheral process of SGNs to be in closer proximity to the CI electrode in order to better approximate natural hearing. This research is in its infancy; it remains to be seen which strategies (surface patterning, small molecule release, hydrogel coating, etc.) will be enable this approach. Additionally, these efforts aimed at optimizing CI function will likely translate to other neural prostheses, which face similar issues.