Neural Engineering Research Research Articles

Hybrid electrical and optical neural interfaces

Neural interfaces bridge the nervous system and the outside world by recording and stimulating neurons. Combining electrical and optical modalities in a single, hybrid neural interface system could lead to complementary and powerful new ways to explore the brain. This convergent approach has gained robust and exciting momentum recently in neuroscience and neural engineering research. Here, we review developments in the past several years aiming to achieve such hybrid electrical and optical microsystem platforms. Specifically, we cover three major categories of technological advances: transparent neuroelectrodes, optical neural fibers with electrodes, and neural probes/grids integrating electrodes and microscale light-emitting diodes. We analyze examples of these probes tailored to combine electrophysiological recording with optical imaging or optical stimulation of the brain and discuss possible directions of future innovation.

Trends of Multimodal Neural Engineering Study: A Bibliometric Review

Neural engineering, an emerging interdisciplinary subject, is aimed at using engineering techniques to investigate the function and manipulate the behavior of the nervous system. The development of technology along with the advancement in Science helps to apply increasing multimodal research into the field of neural engineering, which has promoted the development of neural engineering. In this paper, a bibliometric analysis of 808 articles in Web of Science from 2003 to 2019 was conducted to determine the current status and future trends of multimodal neural engineering study. This paper conducted a five-step bibliometric analysis based on the proposed multimodal neural engineering research framework (NE-MUL). The results showed that in the past 17 years, multimodal research not only made great contributions to the development of neural engineering, but also brought this field a series of new problems (multimodal fusion, recurrent multimodal learning, multimodal convolutional network, etc.) This paper generated a map of existing research findings with their relationship and provided future researchers with meaningful suggestions and assistance.

Application of Continuous Wavelet Transform and Convolutional Neural Network in Decoding Motor Imagery Brain-Computer Interface

The motor imagery-based brain-computer interface (BCI) using electroencephalography (EEG) has been receiving attention from neural engineering researchers and is being applied to various rehabilitation applications. However, the performance degradation caused by motor imagery EEG with very low single-to-noise ratio faces several application issues with the use of a BCI system. In this paper, we propose a novel motor imagery classification scheme based on the continuous wavelet transform and the convolutional neural network. Continuous wavelet transform with three mother wavelets is used to capture a highly informative EEG image by combining time-frequency and electrode location. A convolutional neural network is then designed to both classify motor imagery tasks and reduce computation complexity. The proposed method was validated using two public BCI datasets, BCI competition IV dataset 2b and BCI competition II dataset III. The proposed methods were found to achieve improved classification performance compared with the existing methods, thus showcasing the feasibility of motor imagery BCI.

Laminin-Coated Electrospun Regenerated Silk Fibroin Mats Promote Neural Progenitor Cell Proliferation, Differentiation, and Survival in vitro.

Neural progenitor cell (NPC) transplantation is a promising technique for central nervous system (CNS) reconstruction and regeneration. Biomaterial scaffolds, frameworks, and platforms can support NPC proliferation and differentiation in vitro as well as serve as a temporary extracellular matrix after transplantation. However, further applications of biomaterials require improved biological attributes. Silk fibroin (SF), which is produced by Bombyx mori, is a widely used and studied protein polymer for biomaterial application. Here, we prepared aligned and random eletrospun regenerated SF (RSF) scaffolds, and evaluated their impact on the growth of NPCs. First, we isolated NPCs and then cultured them on either laminin-coated RSF mats or conventional laminin-coated coverslips for cell assays. We found that aligned and random RSF led to increases in NPC proliferation of 143.8 ± 13.3% and 156.3 ± 14.7%, respectively, compared to controls. Next, we investigated neuron differentiation and found that the aligned and the random RSF led to increases in increase in neuron differentiation of about 93.2 ± 6.4%, and 3167.1 ± 4.8%, respectively, compared to controls. Furthermore, we measured the survival of NPCs and found that RSF promoted NPC survival, and found there was no difference among those three groups. Finally, signaling pathways in cells cultured on RSF mats were studied for their contributions in neural cell differentiation. Our results indicate that RSF mats provide a functional microenvironment and represent a useful scaffold for the development of new strategies in neural engineering research.

Neurophysiological predictors and spectro-spatial discriminative features for enhancing SMR-BCI

Neural engineering research is actively engaged in optimizing the robustness of sensorimotor rhythms (SMR)—brain–computer interface (BCI) to boost its potential real-world use. Objective. This paper investigates two vital factors in efficient and robust SMR-BCI design—algorithms that address subject-variability of optimal features and neurophysiological factors that correlate with BCI performance. Existing SMR-BCI research using electroencephalogram (EEG) to classify bilateral motor imagery (MI) focus on identifying subject-specific frequency bands with most discriminative motor patterns localized to sensorimotor region. Approach. A novel strategy to further optimize BCI performance by taking into account the variability of discriminative spectral regions across various EEG channels is proposed in this paper. Main results. The proposed technique results in a significant () increase in average () classification accuracy by accompanied by a considerable reduction in number of channels and bands. The session-to-session transfer variation in spectro-spatial patterns using proposed algorithm is investigated offline and classification performance of the optimized BCI model is successfully evaluated in an online SMR-BCI. Further, the effective prediction of SMR-BCI performance with physiological indicators derived from multi-channel resting-state EEG is demonstrated. The results indicate that the resting state activation patterns such as entropy and gamma power from pre-motor (fronto-central) and posterior (parietal and centro-parietal) areas, and beta power from posterior (centro-parietal) areas estimate BCI performance with minimum error. These patterns, strongly related to BCI performance, may represent certain cognitive states during rest. Significance. Findings reported in this paper imply the need for subject-specific modelling of BCI and the prediction of BCI performance using multi-channel rest-state parameters, to ensure enhanced BCI performance.

Handheld Nerve Electrode Insertion Tool

This paper introduces a new type of miniature device to insert an intrafascicular electrode into a peripheral nervous system that is covered with tough tissue membranes. The developed device is called nerve electrode insertion tool (NEIT) and consists of a nerve holder unit and an electrode insertion unit. Users can easily adjust the insertion position and direction of nerve electrodes while freely manipulating this compact handheld device. This paper introduces the form, configuration, and underlying mechanisms of the device and related surgical procedure. Utility tests using elastomer-made nerve models show that the electrode insertion time and position accuracy, respectively, improve from 72.1 to 4.17 s and 0.37 to 0.11 mm when employing the NEIT. Animal experiment results show that multishank planar electrodes (2 × 2 mm2) are successfully implanted into sciatic nerves of six rats. Treadmill tests and histological analysis present the neural functions of all the treated nerves are successfully recovered after three weeks. We expect the NEIT to be employed for other kinds of nerve electrodes and simplify the surgical process in neural engineering research.

Acquisition of Neural Action Potentials Using Rapid Multiplexing Directly at the Electrodes.

Neural recording systems that interface with implanted microelectrodes are used extensively in experimental neuroscience and neural engineering research. Interface electronics that are needed to amplify, filter, and digitize signals from multichannel electrode arrays are a critical bottleneck to scaling such systems. This paper presents the design and testing of an electronic architecture for intracortical neural recording that drastically reduces the size per channel by rapidly multiplexing many electrodes to a single circuit. The architecture utilizes mixed-signal feedback to cancel electrode offsets, windowed integration sampling to reduce aliased high-frequency noise, and a successive approximation analog-to-digital converter with small capacitance and asynchronous control. Results are presented from a 180 nm CMOS integrated circuit prototype verified using in vivo experiments with a tungsten microwire array implanted in rodent cortex. The integrated circuit prototype achieves <0.004 mm2 area per channel, 7 µW power dissipation per channel, 5.6 µVrms input referred noise, 50 dB common mode rejection ratio, and generates 9-bit samples at 30 kHz per channel by multiplexing at 600 kHz. General considerations are discussed for rapid time domain multiplexing of high-impedance microelectrodes. Overall, this work describes a promising path forward for scaling neural recording systems to numbers of electrodes that are orders of magnitude larger.

Keeping Disability in Mind: A Case Study in Implantable Brain-Computer Interface Research.

Brain-Computer Interface (BCI) research is an interdisciplinary area of study within Neural Engineering. Recent interest in end-user perspectives has led to an intersection with user-centered design (UCD). The goal of user-centered design is to reduce the translational gap between researchers and potential end users. However, while qualitative studies have been conducted with end users of BCI technology, little is known about individual BCI researchers' experience with and attitudes towards UCD. Given the scientific, financial, and ethical imperatives of UCD, we sought to gain a better understanding of practical and principled considerations for researchers who engage with end users. We conducted a qualitative interview case study with neural engineering researchers at a center dedicated to the creation of BCIs. Our analysis generated five themes common across interviews. The thematic analysis shows that participants identify multiple beneficiaries of their work, including other researchers, clinicians working with devices, device end users, and families and caregivers of device users. Participants value experience with device end users, and personal experience is the most meaningful type of interaction. They welcome (or even encourage) end-user input, but are skeptical of limited focus groups and case studies. They also recognize a tension between creating sophisticated devices and developing technology that will meet user needs. Finally, interviewees espouse functional, assistive goals for their technology, but describe uncertainty in what degree of function is "good enough" for individual end users. Based on these results, we offer preliminary recommendations for conducting future UCD studies in BCI and neural engineering.

Beyond ‘communication and control’: towards ethically complete rationales for brain-computer interface research

Rapid advances in neural engineering have led to an increasing use of human subjects in brain-computer interface (BCI) research. Here we ask whether the rationales articulated for this research have kept pace with related ethical requirements. To answer this question, we examined the content of peer-reviewed BCI research publications of studies involving human subjects. We analysed the publications for the rationale expressed for the research against a backdrop of journal type and study design. We discuss the results in the context of strategies that neural engineering researchers can adopt to ensure that the ethical dimensions of human subject research are not lost amid the technological drive for results.

Signal Processing for Neural Spike Trains

Signal Processing for Neural Spike Trains

Translational neural engineering: multiple perspectives on bringing benchtop research into the clinical domain

A half-day forum to address a wide range of issues related to translational neural engineering was conducted at the annual meeting of the Biomedical Engineering Society. Successful practitioners of translational neural engineering from academics, clinical medicine and industry were invited to share a diversity of perspectives and experiences on the translational process. The forum was targeted towards traditional academic researchers who may be interested in the expanded funding opportunities available for translational research that emphasizes product commercialization and clinical implementation. The seminar was funded by the NIH with support from the Rehabilitation Institute of Chicago. We report here a summary of the speaker viewpoints with particular focus on extracting successful strategies for engaging in or conducting translational neural engineering research. Daryl Kipke, PhD, (Department of Biomedical Engineering at the University of Michigan) and Molly Shoichet, PhD, (Department of Chemical Engineering at the University of Toronto) gave details of their extensive experience with product commercialization while holding primary appointments in academic departments. They both encouraged strong clinical input at very early stages of research. Neurosurgeon Fady Charbel, MD, (Department of Neurosurgery at the University of Illinois at Chicago) discussed his role in product commercialization as a clinician. Todd Kuiken, MD, PhD, (Director of the Neural Engineering for Artificial Limbs at the Rehabilitation Institute of Chicago, affiliated with Northwestern University) also a clinician, described a model of translational engineering that emphasized the development of clinically relevant technology, without a strong commercialization imperative. The clinicians emphasized the importance of communicating effectively with engineers. Representing commercial neural engineering was Doug Sheffield, PhD, (Director of New Technology at Vertis Neuroscience, Inc.) who strongly encouraged open industrial–academic partnerships as an efficient path forward in the translational process. Joe Pancrazio, PhD, a Program Director at NIH's National Institute of Neurological Disorders and Stroke, emphasized that NIH funding for translational research was aimed at breaking down scientific barriers to clinic entrance. Vivian Weil, PhD, (Director of Center for the Study of Ethics in the Professions at the Illinois Institute of Technology) a specialist on ethics in science and engineering, spoke of the usefulness of developing a code of ethics for addressing ethical aspects of translation from the bench to clinical implementation and of translation across disciplines in multi-disciplinary projects. Finally, the patient perspective was represented by Mr Jesse Sullivan. A double-arm amputee and patient of Dr Kuiken's, Mr Sullivan demonstrated the critically important role of the patient in successful translational neural engineering research.

Current steering to control the volume of tissue activated during deep brain stimulation

Over the last two decades, deep brain stimulation (DBS) has become a recognized and effective clinical therapy for numerous neurological conditions. Since its inception, clinical DBS technology has progressed at a relatively slow rate; however, advances in neural engineering research have the potential to improve DBS systems. One such advance is the concept of current steering, or the use of multiple stimulation sources to direct current flow through targeted regions of brain tissue. The goals of this study were to develop a theoretical understanding of the effects of current steering in the context of DBS, and use that information to evaluate the potential utility of current steering during stimulation of the subthalamic nucleus. We used finite element electric field models, coupled to multi-compartment cable axon models, to predict the volume of tissue activated (VTA) by DBS as a function of the stimulation parameter settings. Balancing current flow through adjacent cathodes increased the VTA magnitude, relative to monopolar stimulation, and current steering enabled us to sculpt the shape of the VTA to fit a given anatomical target. These results provide motivation for the integration of current steering technology into clinical DBS systems, thereby expanding opportunities to customize DBS to individual patients, and potentially enhancing therapeutic efficacy.