High-density Electrode Arrays Research Articles

Fully desktop fabricated flexible graphene electrocorticography (ECoG) arrays

Objective: Flexible Electrocorticography (ECoG) electrode arrays that conform to the cortical surface and record surface field potentials from multiple brain regions provide unique insights into how computations occurring in distributed brain regions mediate behavior. Specialized microfabrication methods are required to produce flexible ECoG devices with high-density electrode arrays. However, these fabrication methods are challenging for scientists without access to cleanroom fabrication equipment. Results: Here we present a fully desktop fabricated flexible graphene ECoG array. First, we synthesized a stable, conductive ink via liquid exfoliation of Graphene in Cyrene. Next, we established a stencil-printing process for patterning the graphene ink via laser-cut stencils on flexible polyimide substrates. Benchtop tests indicate that the graphene electrodes have good conductivity of ∼1.1 × 103 S cm−1, flexibility to maintain their electrical connection under static bending, and electrochemical stability in a 15 d accelerated corrosion test. Chronically implanted graphene ECoG devices remain fully functional for up to 180 d, with average in vivo impedances of 24.72 ± 95.23 kΩ at 1 kHz. The ECoG device can measure spontaneous surface field potentials from mice under awake and anesthetized states and sensory stimulus-evoked responses. Significance: The stencil-printing fabrication process can be used to create Graphene ECoG devices with customized electrode layouts within 24 h using commonly available laboratory equipment.

Triboelectric Nanogenerator Array as a Probe for In Situ Dynamic Mapping of Interface Charge Transfer at a Liquid-Solid Contacting.

Contact between water droplets with hydrophobic surfaces is a common phenomenon at functional interfaces, and it has been extensively studied. However, quantifying the charge transfer between the liquid-solid interfacial contacting, especially for the charge density distribution throughout the movement of liquid droplet on a dielectric surface, remains to be investigated. Here, we developed a pixeled droplet triboelectric nanogenerator (pixeled droplet-TENG) array with high-density electrode array as a probe for measuring the charge transfer at a liquid-solid interface when a water drop moves on the hydrophobic surface. To intuitively observe the charge transfer between the liquid-solid interface, we "imaged" the transferred charges along movement trajectory of a water droplet as it slides along a tilted solid surface at a spatial resolution of 0.4 mm and time sensitivity of 0.02 s. Our study shows that the transferred charges are not uniformly distributed along the path, which is possibly due to the two-step model of electron transfer and ion adsorbed on the solid surface, and thus the formation of an electric double layer will inevitably shield the net surface on the solid surface. Our study presents a probe technology with potential applications in surface chemistry, physics, material science, and cell biology.

Silent Speech Recognition Based on Surface Electromyography Using a Few Electrode Sites Under the Guidance From High-Density Electrode Arrays

Although surface electromyogram recorded from high-density electrode array is believed to carry sufficient spatial information that can benefit the decoding of motor intentions, the complexity of using the array hindered its widespread applications especially in wearable devices. This study is aimed to develop a non-acoustic modality of silent speech recognition that transfers knowledge learned from high-density array to a system using a few channels, with both high portability and performance. A convolutional neural network was established for recognizing a vocabulary of 33 Chinese words during subvocal speech production. The network was trained by the data recorded from face and neck muscles using two arrays with 64 channels in the source domain. Then it was calibrated through a transfer learning approach to grant its adaption to a new target domain with the data recorded by 8 separated electrodes, while its good capability of characterizing subvocal speech word patterns is expected to be maintained. The proposed method significantly outperformed three common classification approaches and the baseline approach without transfer learning (a network trained with data just from the target domain). Under conditions of electrode shift and cross-user variability, it still obtained performance improvements. The method is demonstrated to be viable for transfer learning across domains of electrode settings and it facilitates to improve the performance of silent speech recognition systems using separate electrode sites under the guidance from high-density of arrays.

Extracting Spatial Muscle Activation Patterns in Facial and Neck Muscles for Silent Speech Recognition Using High-Density sEMG

Human is capable of producing versatile and precise speech owing to the complex neuromuscular systems that control the movement of facial and neck muscle groups. Silent speech recognition based on surface electromyography (sEMG) has been widely researched, which enables speech communication in the absence of an audible acoustic signal. However, the differences in the activities of facial and neck muscle groups under different phoneme pronunciation conditions have not been well investigated. The present work aims to quantify the macroscopic spatial patterns of the muscle activations at the phoneme level during the movement of facial and neck speech articulator muscle groups (SAMGs) using 320-channel high-density (HD) electrode arrays. 10 subjects performed 14 vowel speech tasks and 15 consonant speech tasks in audible speech and silent speech modes, respectively. Specifically, the root-mean-square heat maps of sEMG signals were used to characterize the SAMGs activation patterns. The results show that the muscle activation regions are overlapped under different phoneme pronunciation conditions and symmetric between the left and right sides of face and neck with the average correlation coefficient > 0.9. In addition, the best result can achieve a classification accuracy of 85.78% for consonant and 79.42% for vowel in silent speech mode. The overall results can provide a reference to the hardware or electrode design of portable device for practical use in future work.

A congestion-aware and fault-tolerant readout network ASIC for a high-density electrode array targeting at neutrinoless double-beta decay search in a TPC

Abstract We present a distributed, self-organizing, fault-tolerant and congestion-aware readout network application specific integrated circuit (ASIC) for a high-density electrode array targeting at neutrinoless double-beta decay search in a time projection chamber (TPC). A sensor network is proposed to achieve the readout of the high-density electrode array. This network consists of approximately 1 × 105 single-electrode Topmetal-S sensors with a pitch of approximately 5~10 mm. Static routing is implemented in the first version of the sensor network, without the feature of congestion awareness, which means that only one deterministic path is available for a given packet. Because of the limited memory in each sensor, data will be lost when congestion occurs in this first version of the network. Therefore, the network should have congestion awareness to improve its memory occupancy and effective throughput. Accordingly, a 224 × 224 network with congestion awareness is emulated. The simulation results show that better performance of the sensor network can be achieved with less hardware. An ASIC named adaptive and fault-tolerant readout network chip (AFT-RNC) is implemented with 48.2% of the area of the first implementation.

ADS1299-Based Array Surface Electromyography Signal Acquisition System

A multi-channel sEMG signal acquisition system based on the analog front-end chip ADS1299 is designed. The whole acquisition system consists of a 2×9 high-density electrode array, ADS1299 multi-channel high-precision A/D conversion chip; A MCU named STM32F103C8, an upper computer, and PC. We carried out electrode array design, The introduction of the function of the ADS1299 chip, and the circuit design of the analog signal acquisition part. The test results show that the acquisition system designed in this paper can ideally collect the sEMG signal of 8 channels on the back of the hand, which proves the effectiveness of this design in extracting weak EMG signals. Therefore, it has reference significance for designing larger-scale sEMG signal acquisition circuits.

High-density spinal cord stimulation selectively activates lower urinary tract nerves

Objective. Epidural spinal cord stimulation (SCS) is a potential intervention to improve limb and autonomic functions, with lumbar stimulation improving locomotion and thoracic stimulation regulating blood pressure. Here, we asked whether sacral SCS could be used to target the lower urinary tract (LUT) and used a high-density epidural electrode array to test whether individual electrodes could selectively recruit LUT nerves. Approach. We placed a high-density epidural SCS array on the dorsal surface of the sacral spinal cord and cauda equina of anesthetized cats and recorded the stimulation-evoked activity from nerve cuffs on the pelvic, pudendal and sciatic nerves. Main results. Here we show that sacral SCS evokes responses in nerves innervating the bladder and urethra and that these nerves can be activated selectively. Sacral SCS always recruited the pelvic and pudendal nerves and selectively recruited both of these nerves in all but one animal. Individual branches of the pudendal nerve were always recruited as well. Electrodes that selectively recruited specific peripheral nerves were spatially clustered on the arrays, suggesting anatomically organized sensory pathways. Significance. This selective recruitment demonstrates a mechanism to directly modulate bladder and urethral function through known reflex pathways, which could be used to restore bladder and urethral function after injury or disease.

Blind identification of the spinal cord output in humans with high-density electrode arrays implanted in muscles.

Invasive electromyography opened a new window to explore motoneuron behavior in vivo. However, the technique is limited by the small fraction of active motoneurons that can be concurrently detected, precluding a population analysis in natural tasks. Here, we developed a high-density intramuscular electrode for in vivo human recordings along with a fully automatic methodology that could detect the discharges of action potentials of up to 67 concurrently active motoneurons with 99% accuracy. These data revealed that motoneurons of the same pool receive common synaptic input at frequencies up to 75 Hz and that late-recruited motoneurons inhibit the discharges of those recruited earlier. These results constitute an important step in the population coding analysis of the human motor system in vivo.

Decoding silent speech from high-density surface electromyographic data using transformer

Recent silent speech recognition (SSR) studies based on surface electromyography (sEMG) have been conducted by classifying a finite number of words or phrases without sufficient understanding of temporally semantic information compared to sequential decoding at a fine-grained syllable or phoneme level. This paper presents a syllable-level sequential decoding method using a transformer model for sEMG-based SSR. The proposed method consists of a transformer model and a language model. The input sEMG data was first translated into a sequence of syllable-level decisions by the transformer model. Then, these sequential syllable-level decisions were tuned as a final syllable sequence to approximate natural language through the language model. To verify the effectiveness of the proposed method, experiment data were recorded using two high-density electrode arrays with 64 channels from a total of eight subjects during subvocally reading a corpus of 33 Chinese phrases generated from a dictionary of 82 syllables. The proposed method achieved the lowest character error rate of 5.14 ± 3.28 % and the highest phrase recognition accuracy of 96.37 ± 2.06 %, and it significantly outperformed other common methods for sEMG-based SSR. These findings demonstrated the feasibility and usability of the proposed method for practical SSR applications.

Artefact-Suppressing Analog Spike Detection Circuit for Firing-Rate Measurements in Closed-Loop Retinal Neurostimulators

The aim of this research is to investigate low-power circuit concepts for the hardware implementation of an adaptively controlled stimulator for future retinal implants. For this specific application purpose, the circuit complexity must be low, while at the same time the functionality is extended. This paper presents the implementation of an analog spike detection circuit to detect spikes from extracellular recordings and to perform electrode individual firing-rate measurements in a spatially high-density electrode array, which has a reduced circuit complexity compared to the widely-used nonlinear energy operator (NEO) and allows stronger suppression of local oscillations following the retinal remodeling. The module is verified by emulating extracellular activities using the Hodgkin-Huxley model. This recording-unit is integrated into an eight-channel closed-loop-neurostimulator prototype. It dissipates <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$11.4 ~\mu \text{W}$ </tex-math></inline-formula> and requires an area of 0.066 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> by using a 350 nm CMOS process.

Fabrication of High-Density Vertical Closed Bipolar Electrode Arrays by Carbon Paste Filling Method for Two-Dimensional Chemical Imaging.

In this study, a carbon paste filling method was proposed as a simple strategy for fabricating high-density bipolar electrode (BPE) arrays for bipolar electrochemical microscopy (BEM). High spatiotemporal resolution imaging was achieved using the fabricated BPE array. BEM, which is an emerging microscopic system in recent years, achieves label-free and high spatiotemporal resolution imaging of molecular distributions using high-density BPE arrays and electrochemiluminescence (ECL) signals. We devised a simple method to fabricate a BPE array by filling a porous plate with carbon paste and succeeded in fabricating a high-density BPE array (15 μm pitch). After a detailed observation of the surface of the BPE array using a scanning electron microscope, the basic electrochemical and ECL emission characteristics were evaluated using potassium ferricyanide solution as a sample solution. Moreover, inflow imaging of the sample molecules was conducted to evaluate the imaging ability of the prepared BPE array. In addition, Prussian Blue containing carbon ink was applied to the sample solution side of the BPE array to provide catalytic activity to hydrogen peroxide, and the quantification and inflow imaging of hydrogen peroxide by ECL signals was achieved. This simple fabrication method of the BPE array can accelerate the research and development of BEM. Furthermore, hydrogen peroxide imaging by BEM is an important milestone for achieving bioimaging with high spatiotemporal resolution such as biomolecule imaging using enzymes.

Intraoperative microseizure detection using a high-density micro-electrocorticography electrode array.

One-third of epilepsy patients suffer from medication-resistant seizures. While surgery to remove epileptogenic tissue helps some patients, 30–70% of patients continue to experience seizures following resection. Surgical outcomes may be improved with more accurate localization of epileptogenic tissue. We have previously developed novel thin-film, subdural electrode arrays with hundreds of microelectrodes over a 100–1000 mm2 area to enable high-resolution mapping of neural activity. Here, we used these high-density arrays to study microscale properties of human epileptiform activity. We performed intraoperative micro-electrocorticographic recordings in nine patients with epilepsy. In addition, we recorded from four patients with movement disorders undergoing deep brain stimulator implantation as non-epileptic controls. A board-certified epileptologist identified microseizures, which resembled electrographic seizures normally observed with clinical macroelectrodes. Recordings in epileptic patients had a significantly higher microseizure rate (2.01 events/min) than recordings in non-epileptic subjects (0.01 events/min; permutation test, P = 0.0068). Using spatial averaging to simulate recordings from larger electrode contacts, we found that the number of detected microseizures decreased rapidly with increasing contact diameter and decreasing contact density. In cases in which microseizures were spatially distributed across multiple channels, the approximate onset region was identified. Our results suggest that micro-electrocorticographic electrode arrays with a high density of contacts and large coverage are essential for capturing microseizures in epilepsy patients and may be beneficial for localizing epileptogenic tissue to plan surgery or target brain stimulation.

Ultraflexible and Stretchable Intrafascicular Peripheral Nerve Recording Device with Axon-Dimension, Cuff-Less Microneedle Electrode Array.

Peripheral nerve mapping tools with higher spatial resolution are needed to advance systems neuroscience, and potentially provide a closed-loop biomarker in neuromodulation applications. Two critical challenges of microscale neural interfaces are 1) how to apply them to small peripheral nerves, and 2) how to minimize chronic reactivity. A flexible microneedle nerve array (MINA) is developed, which is the first high-density penetrating electrode array made with axon-sized silicon microneedles embedded in low-modulus thin silicone. The design, fabrication, acute recording, and chronic reactivity to an implanted MINA, are presented. Distinctive units are identified in the rat peroneal nerve. The authors also demonstrate a long-term, cuff-free, and suture-free fixation manner using rose bengal as a light-activated adhesive for two time-points. The tissue response is investigated at 1-week and 6-week time-points, including two sham groups and two MINA-implanted groups. These conditions are quantified in the left vagus nerve of rats using histomorphometry. Micro computed tomography (micro-CT) is added to visualize and quantify tissue encapsulation around the implant. MINA demonstrates a reduction in encapsulation thickness over previously quantified interfascicular methods. Future challenges include techniques for precise insertion of the microneedle electrodes and demonstrating long-term recording.

Rejecting Novel Motions in High-Density Myoelectric Pattern Recognition Using Hybrid Neural Networks.

The objective of this study is to develop a method for alleviating a novel pattern interference toward achieving a robust myoelectric pattern-recognition control system. To this end, a framework was presented for surface electromyogram (sEMG) pattern classification and novelty detection using hybrid neural networks, i.e., a convolutional neural network (CNN) and autoencoder networks. In the framework, the CNN was first used to extract spatio-temporal information conveyed in the sEMG data recorded via high-density (HD) 2-dimensional electrode arrays. Given the target motion patterns well-characterized by the CNN, autoencoder networks were applied to learn variable correlation in the spatio-temporal information, where samples from any novel pattern appeared to be significantly different from those from target patterns. Therefore, it was straightforward to discriminate and then reject the novel motion interferences identified as untargeted and unlearned patterns. The performance of the proposed method was evaluated with HD-sEMG data recorded by two 8 × 6 electrode arrays placed over the forearm extensors and flexors of 9 subjects performing seven target motion tasks and six novel motion tasks. The proposed method achieved high accuracies over 95% for identifying and rejecting novel motion tasks, and it outperformed conventional methods with statistical significance (p < 0.05). The proposed method is demonstrated to be a promising solution for rejecting novel motion interferences, which are ubiquitous in myoelectric control. This study will enhance the robustness of the myoelectric control system against novelty interference.

User-Tailored Hand Gesture Recognition System for Wearable Prosthesis and Armband Based on Surface Electromyogram

Surface electromyogram (sEMG) based hand gesture recognition for prosthesis or armband is an important application of the human-machine interface. However, the measurement location of sensors greatly influences the hand gesture performance, especially with the inter-day or inter-subject validation protocols. Therefore, we acquired two-day hand gesture data of 41 subjects with a 256 (16×16) channel high-density sEMG electrode array. With the acquired data, we initially compared the support vector machine (SVM) and other four state-of-art classifiers under three validation protocols, i.e., intra-day, inter-day and inter-subject validation protocols. Then, we screened 14 feature optimization techniques, including 5 feature-projection methods and 9 feature-ranking approaches. To present the accuracy tendency with varying measure locations, we systematically explored the 10-hand gesture performance using data of 16 prosthesis measurement locations (PMLs) and 15 armband measurement locations (AMLs). As a result, the SVM classifier was suitable for the intra-day and inter-day validation protocols and the 2-dimensional convolutional neural network was selected for the inter-subject validation protocol. The mean accuracies of the hand gesture classification ranged from 95.68% to 99.12% (intra-day validation), from 68.41% to 88.02% (inter-day validation) and from 63.39% to 86.33% (inter-subject validation) for the prosthesis application. In addition, for the armband application, the mean accuracies ranged from 96.25% to 97.43% (intra-day validation), from 67.44% to 75.83% (inter-day validation) and from 65.53% to 75.40% (inter-subject validation). The accuracy is greatly correlated with the measurement location, which is highly associated with the neuromuscular structures of human bodies. In summary, our work can serve as a factor-screening tool for users customizing their systems according to their physical conditions and requirements.

Laser ablation of the pia mater for insertion of high-density microelectrode arrays in a translational sheep model

Objective. The safe insertion of high density intracortical electrode arrays has been a long-standing practical challenge for neural interface engineering and applications such as brain–computer interfaces (BCIs). However, the pia mater can be difficult to penetrate and causes deformation of underlying cortical tissue during insertion of high-density intracortical arrays. This can lead to neuron damage or failed insertions. The development of a method to ease insertion through the pia mater would represent a significant step toward inserting high density intracortical arrays. Approach. Here we describe a surgical procedure, inspired by laser corneal ablation, that can be used in translational models to thin the pia mater. Main results. We demonstrate that controlled pia removal with laser ablation over a small area of cortex allows for microelectrode arrays to be inserted into the cortex with less force, thus reducing deformation of underlying tissue during placement of the microelectrodes. This procedure allows for insertion of high-density electrode arrays and subsequent acute recordings of spiking neuron activity in sheep cortex. We also show histological and electrophysiological evidence that laser removal of the pia does not acutely affect neuronal viability in the region. Significance. Laser ablation of the pia reduces insertion forces of high-density arrays with minimal to no acute damage to cortical neurons. This approach suggests a promising new path for clinical BCI with high-density microelectrode arrays.

Metric learning for novel motion rejection in high-density myoelectric pattern recognition

Most traditional myoelectric pattern recognition systems can only identify limited patterns and are prone to be disturbed by unknown motion tasks. This paper is aimed to develop a robust myoelectric control method towards rejecting novel/unknown patterns. In the proposed method, we first convert high-density surface electromyogram (HD-sEMG) signals into a series of feature images. Next, a metric-learning guided convolutional neural network (CNN) is utilized to extract discriminative representations of the images. Compared to separable representations from common CNN, discriminative property characterizes representations in both the compact intra-class variations and separable inter-class differences. Subsequently, we train multiple autoencoders (AEs) to reject representations from any novel pattern that appeared significantly different from target patterns. The performance of the proposed method was evaluated using HD-sEMG signals recorded by two pieces of flexible 6×8 high-density electrode array placed over forearm extensors and flexors of nine subjects during performing seven target motion tasks and six complicated novel motion tasks. The proposed method can identify and reject novel patterns with high accuracy of 94.28%, which is significantly better than a widely adopted traditional method of 77.49% (p < 0.05). This work demonstrated the validity of applying metric learning in alleviating novel motion interference, which is inevitable in myoelectric control. This work will enhance the robustness of myoelectric control systems.

Evaluation of High-density Multi-channel Electrocorticographic Electrode Arrays in Rat Barrel Cortex

Evaluation of High-density Multi-channel Electrocorticographic Electrode Arrays in Rat Barrel Cortex

Mapping Responses of Lumbar Paravertebral Muscles to Single-Pulse Cortical TMS Using High-Density Surface Electromyography.

Motor evoked potential (MEP), which was elicited by transcranial magnetic stimulation (TMS), has been widely used to detect corticospinal projection from TMS cortical site to trunk muscles. It can help to find the stimulation hotspot in the scalp. However, it fails to precisely describe coordinated activities of trunk muscle groups with only single-channel myoelectric signal. In this study, we aimed to use high-density surface electromyography (sEMG) to explore the effect of cortical TMS on lumbar paravertebral muscles in healthy subjects. The cortical site at 1 cm anterior and 4 cm lateral to vertex was chosen to simulate using a single-pulse TMS with different intensities and forward-bending angles. A high-density electrode array (45 channels) was placed on the surface of lumbar paravertebral muscles to record sEMG signals during a TMS experiment. MEP signals elicited by TMS were extracted from 45-channel recordings and one topographic map of the MEP amplitudes with six spatial features was constructed at each sampling point. The results showed TMS could successfully evoke an oval area with high intensity in the MEP topographic map, while this area mainly located in ipsilateral side of the TMS site. Intensity features related to the high intensity area rose significantly with TMS intensity and forward-bending angle increasing, but location features showed no change. The optimal stimulation parameters were 80% of maximum stimulator output (MSO) for TMS intensity and 30/60 degree for forward-bending angle. This study provided a potentially effective mapping tool to explore the hotspot for transcranial stimulation on trunk muscles.

A distributed readout network ASIC for high-density electrode array targeting at neutrinoless double-beta decay search in a Time Projection Chamber

We present a distributed, self-organizing and fault-tolerant readout network Application Specific Integrated Circuit (ASIC) for the high-density electrode array targeting at neutrinoless double-beta decay search in Time Projection Chamber (TPC). A meter-sized plane tiled by approximately 1×105 single-electrode Topmetal-S sensors with a pitch of 5∼10 mm is utilized to realize charge readout plane in a ton-scale high-pressure gaseous TPC. A sensor network is proposed under the premise of ensuring the tightness and radioactive purity of the high-pressure TPC. As a node of the network, each sensor not only generates and transmits its own data but also forwards the data from its adjacent sensors. Therefore, a Readout and Data-Routing Circuit (RDRC) is integrated into each sensor. To realize the RDRC, a distributed readout network ASIC is fabricated in a 130 nm CMOS Technology. A routing algorithm called Fault-Tolerance XY (FT-XY) is implemented to avoid the faulty sensors from disabling large sections of the network. A single ASIC and a 3 × 3 network have been tested. The test results show that the throughput of the 3 × 3 network reaches 110.52 Mbit/s.