Monitoring Of Neural Signals Research Articles

Enhancing Flexible Neural Probe Performance via Platinum Deposition: Impedance Stability under Various Conditions and In Vivo Neural Signal Monitoring.

Monitoring neural activity in the central nervous system often utilizes silicon-based microelectromechanical system (MEMS) probes. Despite their effectiveness in monitoring, these probes have a fragility issue, limiting their application across various fields. This study introduces flexible printed circuit board (FPCB) neural probes characterized by robust mechanical and electrical properties. The probes demonstrate low impedance after platinum coating, making them suitable for multiunit recordings in awake animals. This capability allows for the simultaneous monitoring of a large population of neurons in the brain, including cluster data. Additionally, these probes exhibit no fractures, mechanical failures, or electrical issues during repeated-bending tests, both during handling and monitoring. Despite the possibility of using this neural probe for signal measurement in awake animals, simply applying a platinum coating may encounter difficulties in chronic tests and other applications. Furthermore, this suggests that FPCB probes can be advanced by any method and serve as an appropriate type of tailorable neural probes for monitoring neural systems in awake animals.

Comparison of electrical microstimulation artifact removal methods for high-channel-count prostheses

BackgroundNeuroprostheses are used to electrically stimulate the brain, modulate neural activity and restore sensory and motor function following injury or disease, such as blindness, paralysis, and other movement and psychiatric disorders. Recordings are often made simultaneously with stimulation, allowing the monitoring of neural signals and closed-loop control of devices. However, stimulation-evoked artifacts may obscure neural activity, particularly when stimulation and recording sites are nearby. Several methods have been developed to remove stimulation artifacts, but it remains challenging to validate and compare these methods because the ‘ground-truth’ of the neuronal signals may be contaminated by artifacts. New methodHere, we delivered stimulation to the visual cortex via a high-channel-count prosthesis while recording neuronal activity and stimulation artifacts. We quantified the waveforms and temporal properties of stimulation artifacts from the cortical visual prosthesis (CVP) and used them to build a dataset, in which we simulated the neuronal activity and the stimulation artifacts. We illustrate how to use the simulated data to evaluate the performance of six software-based artifact removal methods (Template subtraction, Linear interpolation, Polynomial fitting, Exponential fitting, SALPA and ERAASR) in a CVP application scenario. ResultsWe here focused on stimulation artifacts caused by electrical stimulation through a high-channel-count cortical prosthesis device. We find that the Polynomial fitting and Exponential fitting methods outperform the other methods in recovering spikes and multi-unit activity. Linear interpolation and Template subtraction recovered the local-field potentials. ConclusionPolynomial fitting and Exponential fitting provided a good trade-off between the quality of the recovery of spikes and multi-unit activity (MUA) and the computational complexity for a cortical prosthesis.

All Biodisintegratable Hydrogel Biohybrid Neural Interfaces with Synergistic Performances of Microelectrode Array Technologies, Tissue Scaffolding, and Cell Therapy

AbstractBiohybrid neural interfaces (BHNIs) are a new class of neuromodulating devices that integrate neural microelectrode arrays (MEAs) and cell transplantation to improve treatment of nerve injuries and disorders. However, current BHNI devices are made from abiotic materials that are usually bio‐passive, non‐biodisintegratable, or rigid, which restricts encapsulated cell activity and host nerve reconstruction and frequently leads to local tissue inflammation. Herein, the first MEA composed of all disintegratable hydrogel tissue scaffold materials with synergistic performances of tissue conformal adhesiveness, MEA technologies, tissue scaffolding and stem cell therapy on a time scale appropriate for nerve tissue repair is proposed. In particular, the MEA conductive tracks are made from extracellular matrix (ECM)‐based double‐cross‐linked dual‐electrically conductive hydrogel (ECH) systems with robust tissue‐mimicking chemical/physical properties, electrical conductivity, and an affinity for neural progenitor stem cells. Meanwhile, the MEA hydrogel substrate prepared from transglutaminase‐incorporated gelatin/silk precursors simultaneously promotes gelation and interfacial adhesion between all MEA stacks, leading to rapid and scalable device integration. When the full hydrogel MEA is subjected to various mechanical stimuli and moisture, it is structurally stable with a low impedance (4 ± 3 kΩ) comparable to a recently reported benchmark. With seamless lamination around peripheral nerve fibers, the device permits successive neural signal monitoring for wound condition evaluation, while demonstrating synergistic effects of spatiotemporally controlled electrical stimulation and cell transplantation to accelerate restoration of motor function. This BHNI is completely degraded by 1 month thus eliminating the need for surgical retrieval to stably remain, interact, and further fuse with host tissues, successfully exhibiting compatible integration of biology and an implanted electrical system.

A Multi-Channel Neural Recording System With Neural Spike Scan and Adaptive Electrode Selection for High-Density Neural Interface

There is an increasing demand for real-time neural signal monitoring from a large number of electrodes to provide adequate spatial and temporal resolution for high-density brain neural interfaces. This paper proposes an adaptive multi-channel neural recording system that can record neural signals from a large number of electrodes using a smaller number of recording channels. The system utilizes an adaptive electrode selection technique to automatically scan the electrodes where neural spikes occur and record those selected electrodes. A proposed 32-electrode neural recording prototype, including 12 recording channels and 8 scanning channels, was fabricated in a 180-nm CMOS process and tested <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in vitro</i> . With pre-recorded neural data, the system verified that the occurrence of neural spikes in specific electrodes could be detected and processed in real-time, enabling automatic electrode selection. Measured results showed that the proposed system could lead to over 40% reduction in silicon area compared to conventional works.

Thin-Film Electrodes Based on Two-Dimensional Nanomaterials for Neural Interfaces

Detection and monitoring of neural signals is a fast-advancing area of research expected to impact a broad range of advanced applications, from healthcare to brain–machine and even brain-to-brain communications. Two-dimensional (2D) layered materials, such as graphene, MXenes, and transition metal dichalcogenides, could lead to the development of superior and ultrathin thin-film electrodes for neural interfaces thanks to their atomic thickness, high-conductivity properties, and potential to combine additional functionalities. This review focuses on the recent advancement of 2D materials-based thin-film sensors for monitoring of bio-physiological signals using invasive and noninvasive approaches.

A Bootstrapped Pseudo Resistor by Reusing OTA-C Filter for Neural Signal Processing

In integrated neural signal monitoring systems, pseudo resistor (PR) is widely used to form very large time constant filters, due to its large impedance within an acceptable die area. However, its linearity (the dependency of the resistance to applied voltage) and impedance are limited by the nonlinear MOS transistors in weak inversion, and suffers from process, voltage and temperature (PVT) variations. In this brief, a PR bootstrapped by reusing the operational transconductance amplifier-capacitor (OTA-C) filter is presented, it substantially increases the impedance and linearity to dynamically support lower frequency neural signal applications. It has no need for extra bias scheme on the gate or bulk, which decreases the circuit complexity, die area and power consumption. The proposed circuit implemented in a 0.18- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS process occupies an area of 0.026 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , the impedance of the bootstrapped PR is approximately 94 G <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Omega $ </tex-math></inline-formula> , and the high-pass cut-off frequency is reduced to 0.5 mHz.

A Computerized Bioinspired Methodology for Lightweight and Reliable Neural Telemetry.

Personalized health monitoring of neural signals usually results in a very large dataset, the processing and transmission of which require considerable energy, storage, and processing time. We present bioinspired electroceptive compressive sensing (BeCoS) as an approach for minimizing these penalties. It is a lightweight and reliable approach for the compression and transmission of neural signals inspired by active electroceptive sensing used by weakly electric fish. It uses a signature signal and a sensed pseudo-sparse differential signal to transmit and reconstruct the signals remotely. We have used EEG datasets to compare BeCoS with the block sparse Bayesian learning-bound optimization (BSBL-BO) technique—A popular compressive sensing technique used for low-energy wireless telemonitoring of EEG signals. We achieved average coherence, latency, compression ratio, and estimated per-epoch power values that were 35.38%, 62.85%, 53.26%, and 13 mW better than BSBL-BO, respectively, while structural similarity was only 6.295% worse. However, the original and reconstructed signals remain visually similar. BeCoS senses the signals as a derivative of a predefined signature signal resulting in a pseudo-sparse signal that significantly improves the efficiency of the monitoring process. The results show that BeCoS is a promising approach for the health monitoring of neural signals.

A Multi-Channel Neural Recording System with Adaptive Electrode Selection for High-Density Neural Interface.

There is an increasing demand for real-time neural signal monitoring from a large number of electrode contacts to provide adequate spatial and temporal resolution for brain mapping and high-resolution neural interface. This paper proposes a novel multi-channel neural recording system that records neural signals from a large number of electrodes with a smaller number of recording channels. The system utilizes an adaptive electrode selection technique to automatically scan the electrode arrays and record from selected electrodes where neural spikes are detected. The proposed neural recording IC was fabricated in CMOS 180 nm process and tested with in vitro environments. Experiment results with pre-recorded neural data indicate that neural spikes can be separated and amplified with the proposed system and counted in real-time.

Electroceutical Residue-Free Graphene Device for Dopamine Monitoring and Neural Stimulation.

The simultaneous neural signal monitoring and stimulation can allow accurate neurotransmitter regulation for patients in various degrees of neural degeneration disorders. Here, we developed a residue-free graphene device as an effective electrical neural interface for dopamine sensing and secretion. We demonstrated the ultrasensitive dopamine sensing of residue-free graphene devices cultured with PC12 cells and the on-demand functional electrical stimulation for electroceutical applications. The doping effect of graphene by the released dopamine from living cells was confirmed from the electrical current change. The dopamine release could be also quantitatively analyzed by ELISA. Then, Ca2+ ion-dependent dopamine release was optically observed by fluorescence microscopy during the stimulation. Taken together, this study confirms the feasibility of graphene surface as a neural interface for electroceutical applications to various central nerve system disorders.

Gene-Embedded Nanostructural Biotic-Abiotic Optoelectrode Arrays Applied for Synchronous Brain Optogenetics and Neural Signal Recording.

Optogenetics is a recently established neuromodulation technique in which photostimulation is used to manipulate neurons with high temporal and spatial precision. However, sequential genetic and optical insertion with double brain implantation tends to cause excessive tissue damage. In addition, the incorporation of light-sensitive genes requires the utilization of viral vectors, which remains a safety concern. Here, by combining device fabrication design, nanotechnology, and cell targeting technology, we developed a new gene-embedded optoelectrode array for neural implantation to enable spatiotemporal electroporation (EP) for gene delivery/transfection, photomodulation, and synchronous electrical monitoring of neural signals in the brain via one-time implantation. A biotic-abiotic neural interface (called PG) composed of reduced graphene oxide and conductive polyelectrolyte 3,4-ethylenedioxythiophene-modified amphiphilic chitosan was developed to form a nanostructural hydrogel with assembled nanodomains for encapsulating nonviral gene vectors (called PEI-NT-pDNA) formulated by neurotensin (NT) and polyethylenimine (PEI)-coupled plasmid DNA (pDNA). The PG can maintain high charge storage ability to respond to a minimal current of 125 μA for controllable gene delivery. The in vitro analysis of PG-PEI-NT-pDNA on the microelectrode array chip showed that the microelectrodes provided electrically inductive electropermeabilization, which permitted gene transfection into localized rat adrenal pheochromocytoma cells with a strong green fluorescent protein expression that was up to 8-fold higher than that in nontreated cells. Furthermore, the in vivo implantation enabled on-demand spatiotemporal gene transfection to neurons with 10-fold enhancement of targeting ability compared with astrocytes. Finally, using the real optogenetic opsin channelrhodopsin-2, the flexible neural probe incorporated with an optical waveguide fiber displayed photoevoked extracellular spikes in the thalamic ventrobasal region after focal EP for only 7 days, which provided a proof of concept for the use of photomodulation to facilitate neural therapies.

A Hybrid 3D Printing and Robotic-assisted Embedding Approach for Design and Fabrication of Nerve Cuffs with Integrated Locking Mechanisms

ABSTRACTThe ability to interface electronic materials with the peripheral nervous system is required for stimulation and monitoring of neural signals. Thus, the design and engineering of robust neural interfaces that maintain material-tissue contact in the presence of material or tissue micromotion offer the potential to conduct novel measurements and develop future therapies that require chronic interface with the peripheral nervous system. However, such remains an open challenge given the constraints of existing materials sets and manufacturing approaches for design and fabrication of neural interfaces. Here, we investigated the potential to leverage a rapid prototyping approach for the design and fabrication of nerve cuffs that contain supporting features to mechanically stabilize the interaction between cuff electrodes and peripheral nerve. A hybrid 3D printing and robotic-embedding (i.e., pick-and-place) system was used to design and fabricate silicone nerve cuffs (800 µm diameter) containing conforming platinum (Pt) electrodes. We demonstrate that the electrical impedance of the cuff electrodes can be reduced by deposition of the conducting polymer poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) on cuff electrodes via a post-processing electropolymerization technique. The computer-aided design and manufacturing approach was also used to design and integrate supporting features to the cuff that mechanically stabilize the interface between the cuff electrodes and the peripheral nerve. Both ‘self-locking’ and suture-assisted locking mechanisms are demonstrated based on the principle of making geometric alterations to the cuff opening via 3D printing. Ultimately, this work shows 3D printing offers considerable opportunity to integrate supporting features, and potentially even novel electronic materials, into nerve cuffs that can support the design and engineering of next generation neural interfaces.

Brain activity mapping at multiple scales with silicon microprobes containing 1,024 electrodes.

The coordinated activity of neural ensembles across multiple interconnected regions has been challenging to study in the mammalian brain with cellular resolution using conventional recording tools. For instance, neural systems regulating learned behaviors often encompass multiple distinct structures that span the brain. To address this challenge we developed a three-dimensional (3D) silicon microprobe capable of simultaneously measuring extracellular spike and local field potential activity from 1,024 electrodes. The microprobe geometry can be precisely configured during assembly to target virtually any combination of four spatially distinct neuroanatomical planes. Here we report on the operation of such a device built for high-throughput monitoring of neural signals in the orbitofrontal cortex and several nuclei in the basal ganglia. We perform analysis on systems-level dynamics and correlations during periods of conditioned behavioral responding and rest, demonstrating the technology's ability to reveal functional organization at multiple scales in parallel in the mouse brain.

Bandwidth Resource Management for Neural Signal Telemetry

Recent advances in modern neurocomputing have shown the utmost necessity of wireless communication systems that allow real-time (RT) monitoring of neural signals meeting several requirements such as source compression and high fidelity of the received signal. Neural recordings require multielectrode probes with up to hundreds of electrodes and transmission of signals wirelessly over a limited bandwidth (BW). In this paper, a RT resource management algorithm is proposed so that adequate source compression is applied to each channel in order to fit them into the available BW. Performance of the algorithm is analyzed using dynamically changing BW and neural recordings with different neural activity characteristics.

Ultra wideband for wireless real-time monitoring of neural signals

Performance of an ultra wideband (UWB) wireless system for real-time neural signal monitoring is evaluated by comparing spiking characteristics between transmitted and received signals for different experimental set-ups. Spike detection quality is selected as the main spiking characteristic of evaluated signals. Results are presented in receiver-operating characteristics and area-under-the-curve (AUC). In order to assess spike detection quality, a set of artificially generated neural signals is constructed from real neural recordings such that the ground truth is known. Data analysis shows how channel signal-to-noise-ratio (SNR) variation affects AUC in different signal SNR cases. Signals with low SNRs get less affected by reduced channel SNRs than those with higher SNR. Increasing bit error rate modifies spiking characteristics such that an under-estimation of the spiking frequency occurs due to spike losses. For practical application of real-time neural signal monitoring, UWB seems to offer best transmission conditions in a near-body environment.

Multi-Channel Optical Monitoring of Neural Signals from Aplysia Ganglion

Multi-Channel Optical Monitoring of Neural Signals from Aplysia Ganglion