Collaborative approach in the development of high-performance brain-computer interfaces for a neuroprosthetic arm: translation from animal models to human control

Electrode sharpness and insertion speed reduce tissue damage near high-density penetrating arrays

Objective. Over the past decade, neural electrodes have played a crucial role in bridging biological tissues with electronic and robotic devices. This study focuses on evaluating the optimal tip profile and insertion speed for effectively implanting Paradromics' high-density fine microwire arrays (FμA) prototypes into the primary visual cortex (V1) of mice and rats, addressing the challenges associated with the 'bed-of-nails' effect and tissue dimpling.Approach. Tissue response was assessed by investigating the impact of electrodes on the blood-brain barrier (BBB) and cellular damage, with a specific emphasis on tailored insertion strategies to minimize tissue disruption during electrode implantation.Main results.Electro-sharpened arrays demonstrated a marked reduction in cellular damage within 50μm of the electrode tip compared to blunt and angled arrays. Histological analysis revealed that slow insertion speeds led to greater BBB compromise than fast and pneumatic methods. Successful single-unit recordings validated the efficacy of the optimized electro-sharpened arrays in capturing neural activity.Significance.These findings underscore the critical role of tailored insertion strategies in minimizing tissue damage during electrode implantation, highlighting the suitability of electro-sharpened arrays for long-term implant applications. This research contributes to a deeper understanding of the complexities associated with high-channel-count microelectrode array implantation, emphasizing the importance of meticulous assessment and optimization of key parameters for effective integration and minimal tissue disruption. By elucidating the interplay between insertion parameters and tissue response, our study lays a strong foundation for the development of advanced implantable devices with a reduction in reactive gliosis and improved performance in neural recording applications.

Chronic stability of a neuroprosthesis comprising multiple adjacent Utah arrays in monkeys

Objective. Electrical stimulation of visual cortex via a neuroprosthesis induces the perception of dots of light ('phosphenes'), potentially allowing recognition of simple shapes even after decades of blindness. However, restoration of functional vision requires large numbers of electrodes, and chronic, clinical implantation of intracortical electrodes in the visual cortex has only been achieved using devices of up to 96 channels. We evaluated the efficacy and stability of a 1024-channel neuroprosthesis system in non-human primates (NHPs) over more than 3 years to assess its suitability for long-term vision restoration.Approach.We implanted 16 microelectrode arrays (Utah arrays) consisting of 8 × 8 electrodes with iridium oxide tips in the primary visual cortex (V1) and visual area 4 (V4) of two sighted macaques. We monitored the animals' health and measured electrode impedances and neuronal signal quality by calculating signal-to-noise ratios of visually driven neuronal activity, peak-to-peak voltages of the waveforms of action potentials, and the number of channels with high-amplitude signals. We delivered cortical microstimulation and determined the minimum current that could be perceived, monitoring the number of channels that successfully yielded phosphenes. We also examined the influence of the implant on a visual task after 2-3 years of implantation and determined the integrity of the brain tissue with a histological analysis 3-3.5 years post-implantation.Main results. The monkeys remained healthy throughout the implantation period and the device retained its mechanical integrity and electrical conductivity. However, we observed decreasing signal quality with time, declining numbers of phosphene-evoking electrodes, decreases in electrode impedances, and impaired performance on a visual task at visual field locations corresponding to implanted cortical regions. Current thresholds increased with time in one of the two animals. The histological analysis revealed encapsulation of arrays and cortical degeneration. Scanning electron microscopy on one array revealed degradation of IrOxcoating and higher impedances for electrodes with broken tips.Significance. Long-term implantation of a high-channel-count device in NHP visual cortex was accompanied by deformation of cortical tissue and decreased stimulation efficacy and signal quality over time. We conclude that improvements in device biocompatibility and/or refinement of implantation techniques are needed before future clinical use is feasible.

The effects of layer-wise relevance propagation-based feature selection for EEG classification: a comparative study on multiple datasets

Introduction

The brain-computer interface (BCI) allows individuals to control external devices using their neural signals. One popular BCI paradigm is motor imagery (MI), which involves imagining movements to induce neural signals that can be decoded to control devices according to the user's intention. Electroencephalography (EEG) is frequently used for acquiring neural signals from the brain in the fields of MI-BCI due to its non-invasiveness and high temporal resolution. However, EEG signals can be affected by noise and artifacts, and patterns of EEG signals vary across different subjects. Therefore, selecting the most informative features is one of the essential processes to enhance classification performance in MI-BCI.

Methods

In this study, we design a layer-wise relevance propagation (LRP)-based feature selection method which can be easily integrated into deep learning (DL)-based models. We assess its effectiveness for reliable class-discriminative EEG feature selection on two different publicly available EEG datasets with various DL-based backbone models in the subject-dependent scenario.

Results and discussion

The results show that LRP-based feature selection enhances the performance for MI classification on both datasets for all DL-based backbone models. Based on our analysis, we believe that it can broad its capability to different research domains.

Coherence based graph convolution network for motor imagery-induced EEG after spinal cord injury

Background

Spinal cord injury (SCI) may lead to impaired motor function, autonomic nervous system dysfunction, and other dysfunctions. Brain-computer Interface (BCI) system based on motor imagery (MI) can provide more scientific and effective treatment solutions for SCI patients.

Methods

According to the interaction between brain regions, a coherence-based graph convolutional network (C-GCN) method is proposed to extract the temporal-frequency-spatial features and functional connectivity information of EEG signals. The proposed algorithm constructs multi-channel EEG features based on coherence networks as graphical signals and then classifies MI tasks. Different from the traditional graphical convolutional neural network (GCN), the C-GCN method uses the coherence network of EEG signals to determine MI-related functional connections, which are used to represent the intrinsic connections between EEG channels in different rhythms and different MI tasks. EEG data of SCI patients and healthy subjects have been analyzed, where healthy subjects served as the control group.

Results

The experimental results show that the C-GCN method can achieve the best classification performance with certain reliability and stability, the highest classification accuracy is 96.85%.

Conclusion

The proposed framework can provide an effective theoretical basis for the rehabilitation treatment of SCI patients.

Hybrid mental tasks based human computer interface via integration of pronunciation and motor imagery

Objective.Among the existing active brain-computer interfaces (BCI), the motor imagination (MI) is widely used. To operate the MI BCI effectively, subjects need to carry out trainings on corresponding imagining tasks. Here, we studied how to reduce the discomfort and fatigue of active BCI imaginary tasks and the inability to concentrate on them while improving the accuracy.Approach.This paper proposes a hybrid BCI composed of MI and pronunciation imagination (PI). The electroencephalogram signals of ten subjects are recognized by the adaptive Riemannian distance classification and the improved frequency selective filter-bank Common Spatial Pattern recognition.Main results.The results show that under the new paradigm with the combination of MI and PI, the recognition accuracy is higher than the MI alone. The highest recognition rate of the proposed hybrid system can reach more than 90%. Furthermore, through the subjects' scoring results of the operation difficulty, it is concluded that the designed hybrid paradigm is more operable than the traditional BCI paradigm.Significance.The separable tasks in the active BCI are limited and the accuracy needs to be improved. The new hybrid paradigm proposed by us improves the accuracy and operability of the active BCI system, providing a new possibility for the research direction of the active BCI.

In vivo spatiotemporal dynamics of astrocyte reactivity following neural electrode implantation

Brain computer interfaces (BCIs), including penetrating microelectrode arrays, enable both recording and stimulation of neural cells. However, device implantation inevitably causes injury to brain tissue and induces a foreign body response, leading to reduced recording performance and stimulation efficacy. Astrocytes in the healthy brain play multiple roles including regulating energy metabolism, homeostatic balance, transmission of neural signals, and neurovascular coupling. Following an insult to the brain, they are activated and gather around the site of injury. These reactive astrocytes have been regarded as one of the main contributors to the formation of a glial scar which affects the performance of microelectrode arrays. This study investigates the dynamics of astrocytes within the first 2 weeks after implantation of an intracortical microelectrode into the mouse brain using two-photon microscopy. From our observation astrocytes are highly dynamic during this period, exhibiting patterns of process extension, soma migration, morphological activation, and device encapsulation that are spatiotemporally distinct from other glial cells, such as microglia or oligodendrocyte precursor cells. This detailed characterization of astrocyte reactivity will help to better understand the tissue response to intracortical devices and lead to the development of more effective intervention strategies to improve the functional performance of neural interfacing technology.

Ethical commitments, principles, and practices guiding intracranial neuroscientific research in humans

Leveraging firsthand experience, BRAIN-funded investigators conducting intracranial human neuroscience research propose two fundamental ethical commitments: (1) maintaining the integrity of clinical care and (2) ensuring voluntariness. Principles, practices, and uncertainties related to these commitments are offered for future investigation.

Case Study: Mapping Evoked Fields in Primary Motor and Sensory Areas via Magnetoencephalography in Tetraplegia

Devices interfacing with the brain through implantation in cortical or subcortical structures have great potential for restoration and rehabilitation in patients with sensory or motor dysfunction. Typical implantation surgeries are planned based on maps of brain activity generated from intact function. However, mapping brain activity for planning implantation surgeries is challenging in the target population due to abnormal residual function and, increasingly often, existing MRI-incompatible implanted hardware. Here, we present methods and results for mapping impaired somatosensory and motor function in an individual with paralysis and an existing brain–computer interface (BCI) device. Magnetoencephalography (MEG) was used to directly map the neural activity evoked during transcutaneous electrical stimulation and attempted movement of the impaired hand. Evoked fields were found to align with the expected anatomy and somatotopic organization. This approach may be valuable for guiding implants in other applications, such as cortical stimulation for pain and to improve implant targeting to help reduce the craniotomy size.

Finding Kinematics-Driven Latent Neural States From Neuronal Population Activity for Motor Decoding

While intracortical brain-machine interfaces (BMIs) demonstrate feasibility to restore mobility to people with paralysis, it is still challenging to maintain high-performance decoding in clinical BMIs. One of the main obstacles for high-performance BMI is the noise-prone nature of traditional decoding methods that connect neural response explicitly with physical quantity, such as velocity. In contrast, the recent development of latent neural state model enables a robust readout of large-scale neuronal population activity contents. However, these latent neural states do not necessarily contain kinematic information useful for decoding. Therefore, this study proposes a new approach to finding kinematics-dependent latent factors by extracting latent factors' kinematics-dependent components using linear regression. We estimated these components from the population activity through nonlinear mapping. The proposed kinematics-dependent latent factors generate neural trajectories that discriminate latent neural states before and after the motion onset. We compared the decoding performance of the proposed analysis model with the results from other popular models. They are factor analysis (FA), Gaussian process factor analysis (GPFA), latent factor analysis via dynamical systems (LFADS), preferential subspace identification (PSID), and neuronal population firing rates. The proposed analysis model results in higher decoding accuracy than do the others ( % improvement on average). Our approach may pave a new way to extract latent neural states specific to kinematic information from motor cortices, potentially improving decoding performance for online intracortical BMIs.

Meeting Proceedings for SCI 2020: Launching a Decade of Disruption in Spinal Cord Injury Research

The spinal cord injury (SCI) research community has experienced great advances in discovery research, technology development, and promising clinical interventions in the past decade. To build upon these advances and maximize the benefit to persons with SCI, the National Institutes of Health (NIH) hosted a conference February 12-13, 2019 titled "SCI 2020: Launching a Decade of Disruption in Spinal Cord Injury Research." The purpose of the conference was to bring together a broad range of stakeholders, including researchers, clinicians and healthcare professionals, persons with SCI, industry partners, regulators, and funding agency representatives to break down existing communication silos. Invited speakers were asked to summarize the state of the science, assess areas of technological and community readiness, and build collaborations that could change the trajectory of research and clinical options for people with SCI. In this report, we summarize the state of the science in each of five key domains and identify the gaps in the scientific literature that need to be addressed to move the field forward.

A skin-inspired tactile sensor for smart prosthetics

Recent achievements in the field of electronic skin have provided promising technology for prosthetic systems. However, the development of a bionic tactile-perception system that exhibits integrated stimuli sensing and neuron-like information-processing functionalities in a low-pressure regime remains a challenge. Here, we demonstrate a tactile sensor for smart prosthetics based on giant magneto-impedance (GMI) material embedded with an air gap. The sensor exhibits a high sensitivity of 120 newton^-1 (or 4.4 kilopascal^-1) and a very low detection limit of 10 micronewtons (or 0.3 pascals). The integration of the tactile sensor with an inductance-capacitance (LC) oscillation circuit enabled direct transduction of force stimuli into digital-frequency signals. The frequency increased with the force stimuli, consistent with the relationship between stimuli and human responses. The minimum loading of 50 micronewtons (or 1.25 pascals), which is less than the sensing threshold value of human skin, was also encoded into the frequency, similar to the pulse waveform of humans. The proposed tactile sensor not only showed desirable sensitivity and low detection limit but also exhibited transduction of digital-frequency signals like human stimuli responses. These features of the GMI-based tactile sensor show potential for its applications in smart prosthetics, especially prosthetic limbs that can functionally replace natural limbs.

Summary of over Fifty Years with Brain-Computer Interfaces-A Review

Over the last few decades, the Brain-Computer Interfaces have been gradually making their way to the epicenter of scientific interest. Many scientists from all around the world have contributed to the state of the art in this scientific domain by developing numerous tools and methods for brain signal acquisition and processing. Such a spectacular progress would not be achievable without accompanying technological development to equip the researchers with the proper devices providing what is absolutely necessary for any kind of discovery as the core of every analysis: the data reflecting the brain activity. The common effort has resulted in pushing the whole domain to the point where the communication between a human being and the external world through BCI interfaces is no longer science fiction but nowadays reality. In this work we present the most relevant aspects of the BCIs and all the milestones that have been made over nearly 50-year history of this research domain. We mention people who were pioneers in this area as well as we highlight all the technological and methodological advances that have transformed something available and understandable by a very few into something that has a potential to be a breathtaking change for so many. Aiming to fully understand how the human brain works is a very ambitious goal and it will surely take time to succeed. However, even that fraction of what has already been determined is sufficient e.g., to allow impaired people to regain control on their lives and significantly improve its quality. The more is discovered in this domain, the more benefit for all of us this can potentially bring.

Adenosine A2A receptor blockade improves neuroprosthetic learning by volitional control of population calcium signal in M1 cortical neurons

Volitional control is at the core of brain-machine interfaces (BMI) adaptation and neuroprosthetic-driven learning to restore motor function for disabled patients, but neuroplasticity changes and neuromodulation underlying volitional control of neuroprosthetic learning are largely unexplored. To better study volitional control at annotated neural population, we have developed an operant neuroprosthetic task with closed-loop feedback system by volitional conditioning of population calcium signal in the M1 cortex using fiber photometry recording. Importantly, volitional conditioning of the population calcium signal in M1 neurons did not improve within-session adaptation, but specifically enhanced across-session neuroprosthetic skill learning with reduced time-to-target and the time to complete 50 successful trials. With brain-behavior causality of the neuroprosthetic paradigm, we revealed that proficiency of neuroprosthetic learning by volitional conditioning of calcium signal was associated with the stable representational (plasticity) mapping in M1 neurons with the reduced calcium peak. Furthermore, pharmacological blockade of adenosine A_2A receptors facilitated volitional conditioning of neuroprosthetic learning and converted an ineffective volitional conditioning protocol to be the effective for neuroprosthetic learning. These findings may help to harness neuroplasticity for better volitional control of neuroprosthetic training and suggest a novel pharmacological strategy to improve neuroprosthetic learning in BMI adaptation by targeting striatal A_2A receptors.

Electrical Stimulation as a Tool to Promote Plasticity of the Injured Spinal Cord

Unlike their peripheral nervous system counterparts, the capacity of central nervous system neurons and axons for regeneration after injury is minimal. Although a myriad of therapies (and different combinations thereof) to help promote repair and recovery after spinal cord injury (SCI) have been trialed, few have progressed from bench-top to bedside. One of the few such therapies that has been successfully translated from basic science to clinical applications is electrical stimulation (ES). Although the use and study of ES in peripheral nerve growth dates back nearly a century, only recently has it started to be used in a clinical setting. Since those initial experiments and seminal publications, the application of ES to restore function and promote healing have greatly expanded. In this review, we discuss the progression and use of ES over time as it pertains to promoting axonal outgrowth and functional recovery post-SCI. In doing so, we consider four major uses for the study of ES based on the proposed or documented underlying mechanism: (1) using ES to introduce an electric field at the site of injury to promote axonal outgrowth and plasticity; (2) using spinal cord ES to activate or to increase the excitability of neuronal networks below the injury; (3) using motor cortex ES to promote corticospinal tract axonal outgrowth and plasticity; and (4) leveraging the timing of paired stimuli to produce plasticity. Finally, the use of ES in its current state in the context of human SCI studies is discussed, in addition to ongoing research and current knowledge gaps, to highlight the direction of future studies for this therapeutic modality.

Noninvasive neuroimaging enhances continuous neural tracking for robotic device control

Brain-computer interfaces (BCIs) utilizing signals acquired with intracortical implants have achieved successful high-dimensional robotic device control useful for completing daily tasks. However, the substantial amount of medical and surgical expertise required to correctly implant and operate these systems significantly limits their use beyond a few clinical cases. A noninvasive counterpart requiring less intervention that can provide high-quality control would profoundly impact the integration of BCIs into the clinical and home setting. Here, we present and validate a noninvasive framework utilizing electroencephalography (EEG) to achieve the neural control of a robotic device for continuous random target tracking. This framework addresses and improves upon both the "brain" and "computer" components by respectively increasing user engagement through a continuous pursuit task and associated training paradigm, and the spatial resolution of noninvasive neural data through EEG source imaging. In all, our unique framework enhanced BCI learning by nearly 60% for traditional center-out tasks and by over 500% in the more realistic continuous pursuit task. We further demonstrated an additional enhancement in BCI control of almost 10% by using online noninvasive neuroimaging. Finally, this framework was deployed in a physical task, demonstrating a near seamless transition from the control of an unconstrained virtual cursor to the real-time control of a robotic arm. Such combined advances in the quality of neural decoding and the practical utility of noninvasive robotic arm control will have major implications on the eventual development and implementation of neurorobotics by means of noninvasive BCI.

The History and Horizons of Microscale Neural Interfaces

Microscale neural technologies interface with the nervous system to record and stimulate brain tissue with high spatial and temporal resolution. These devices are being developed to understand the mechanisms that govern brain function, plasticity and cognitive learning, treat neurological diseases, or monitor and restore functions over the lifetime of the patient. Despite decades of use in basic research over days to months, and the growing prevalence of neuromodulation therapies, in many cases the lack of knowledge regarding the fundamental mechanisms driving activation has dramatically limited our ability to interpret data or fine-tune design parameters to improve long-term performance. While advances in materials, microfabrication techniques, packaging, and understanding of the nervous system has enabled tremendous innovation in the field of neural engineering, many challenges and opportunities remain at the frontiers of the neural interface in terms of both neurobiology and engineering. In this short-communication, we explore critical needs in the neural engineering field to overcome these challenges. Disentangling the complexities involved in the chronic neural interface problem requires simultaneous proficiency in multiple scientific and engineering disciplines. The critical component of advancing neural interface knowledge is to prepare the next wave of investigators who have simultaneous multi-disciplinary proficiencies with a diverse set of perspectives necessary to solve the chronic neural interface challenge.

Multi-scale, multi-modal analysis uncovers complex relationship at the brain tissue-implant neural interface: new emphasis on the biological interface

OBJECTIVE

Implantable neural electrode devices are important tools for neuroscience research and have an increasing range of clinical applications. However, the intricacies of the biological response after implantation, and their ultimate impact on recording performance, remain challenging to elucidate. Establishing a relationship between the neurobiology and chronic recording performance is confounded by technical challenges related to traditional electrophysiological, material, and histological limitations. This can greatly impact the interpretations of results pertaining to device performance and tissue health surrounding the implant.

APPROACH

In this work, electrophysiological activity and immunohistological analysis are compared after controlling for motion artifacts, quiescent neuronal activity, and material failure of devices in order to better understand the relationship between histology and electrophysiological outcomes.

MAIN RESULTS

Even after carefully accounting for these factors, the presence of viable neurons and lack of glial scarring does not convey single unit recording performance.

SIGNIFICANCE

To better understand the biological factors influencing neural activity, detailed cellular and molecular tissue responses were examined. Decreases in neural activity and blood oxygenation in the tissue surrounding the implant, shift in expression levels of vesicular transporter proteins and ion channels, axon and myelin injury, and interrupted blood flow in nearby capillaries can impact neural activity around implanted neural interfaces. Combined, these tissue changes highlight the need for more comprehensive, basic science research to elucidate the relationship between biology and chronic electrophysiology performance in order to advance neural technologies.

Real-time visuomotor behavior and electrophysiology recording setup for use with humans and monkeys

Large-scale network dynamics in multiple visuomotor areas is of great interest in the study of eye-hand coordination in both human and monkey. To explore this, it is essential to develop a setup that allows for precise tracking of eye and hand movements. It is desirable that it is able to generate mechanical or visual perturbations of hand trajectories so that eye-hand coordination can be studied in a variety of conditions. There are simple solutions that satisfy these requirements for hand movements performed in the horizontal plane while visual stimuli and hand feedback are presented in the vertical plane. However, this spatial dissociation requires cognitive rules for eye-hand coordination different from eye-hand movements performed in the same space, as is the case in most natural conditions. Here we present an innovative solution for the precise tracking of eye and hand movements in a single reference frame. Importantly, our solution allows behavioral explorations under normal and perturbed conditions in both humans and monkeys. It is based on the integration of two noninvasive commercially available systems to achieve online control and synchronous recording of eye (EyeLink) and hand (KINARM) positions during interactive visuomotor tasks. We also present an eye calibration method compatible with different eye trackers that compensates for nonlinearities caused by the system's geometry. Our setup monitors the two effectors in real time with high spatial and temporal resolution and simultaneously outputs behavioral and neuronal data to an external data acquisition system using a common data format.

NEW & NOTEWORTHY We developed a new setup for studying eye-hand coordination in humans and monkeys that monitors the two effectors in real time in a common reference frame. Our eye calibration method allows us to track gaze positions relative to visual stimuli presented in the horizontal workspace of the hand movements. This method compensates for nonlinearities caused by the system’s geometry and transforms kinematics signals from the eye tracker into the same coordinate system as hand and targets.

Remapping cortical modulation for electrocorticographic brain-computer interfaces: a somatotopy-based approach in individuals with upper-limb paralysis

OBJECTIVE

Brain-computer interface (BCI) technology aims to provide individuals with paralysis a means to restore function. Electrocorticography (ECoG) uses disc electrodes placed on either the surface of the dura or the cortex to record field potential activity. ECoG has been proposed as a viable neural recording modality for BCI systems, potentially providing stable, long-term recordings of cortical activity with high spatial and temporal resolution. Previously we have demonstrated that a subject with spinal cord injury (SCI) could control an ECoG-based BCI system with up to three degrees of freedom (Wang et al 2013 PLoS One). Here, we expand upon these findings by including brain-control results from two additional subjects with upper-limb paralysis due to amyotrophic lateral sclerosis and brachial plexus injury, and investigate the potential of motor and somatosensory cortical areas to enable BCI control.

APPROACH

Individuals were implanted with high-density ECoG electrode grids over sensorimotor cortical areas for less than 30 d. Subjects were trained to control a BCI by employing a somatotopic control strategy where high-gamma activity from attempted arm and hand movements drove the velocity of a cursor.

MAIN RESULTS

Participants were capable of generating robust cortical modulation that was differentiable across attempted arm and hand movements of their paralyzed limb. Furthermore, all subjects were capable of voluntarily modulating this activity to control movement of a computer cursor with up to three degrees of freedom using the somatotopic control strategy. Additionally, for those subjects with electrode coverage of somatosensory cortex, we found that somatosensory cortex was capable of supporting ECoG-based BCI control.

SIGNIFICANCE

These results demonstrate the feasibility of ECoG-based BCI systems for individuals with paralysis as well as highlight some of the key challenges that must be overcome before such systems are translated to the clinical realm. ClinicalTrials.gov Identifier: NCT01393444.

A Materials Roadmap to Functional Neural Interface Design

Advancement in neurotechnologies for electrophysiology, neurochemical sensing, neuromodulation, and optogenetics are revolutionizing scientific understanding of the brain while enabling treatments, cures, and preventative measures for a variety of neurological disorders. The grand challenge in neural interface engineering is to seamlessly integrate the interface between neurobiology and engineered technology, to record from and modulate neurons over chronic timescales. However, the biological inflammatory response to implants, neural degeneration, and long-term material stability diminish the quality of interface overtime. Recent advances in functional materials have been aimed at engineering solutions for chronic neural interfaces. Yet, the development and deployment of neural interfaces designed from novel materials have introduced new challenges that have largely avoided being addressed. Many engineering efforts that solely focus on optimizing individual probe design parameters, such as softness or flexibility, downplay critical multi-dimensional interactions between different physical properties of the device that contribute to overall performance and biocompatibility. Moreover, the use of these new materials present substantial new difficulties that must be addressed before regulatory approval for use in human patients will be achievable. In this review, the interdependence of different electrode components are highlighted to demonstrate the current materials-based challenges facing the field of neural interface engineering.

Human perception of electrical stimulation on the surface of somatosensory cortex

Recent advancement in electrocorticography (ECoG)-based brain-computer interface technology has sparked a new interest in providing somatosensory feedback using ECoG electrodes, i.e., cortical surface electrodes. We conducted a 28-day study of cortical surface stimulation in an individual with arm paralysis due to brachial plexus injury to examine the sensation produced by electrical stimulation of the somatosensory cortex. A high-density ECoG grid was implanted over the somatosensory and motor cortices. Stimulation through cortical surface electrodes over the somatosensory cortex successfully elicited arm and hand sensations in our participant with chronic paralysis. There were three key findings. First, the intensity of perceived sensation increased monotonically with both pulse amplitude and pulse frequency. Second, changing pulse width changed the type of sensation based on qualitative description provided by the human participant. Third, the participant could distinguish between stimulation applied to two neighboring cortical surface electrodes, 4.5 mm center-to-center distance, for three out of seven electrode pairs tested. Taken together, we found that it was possible to modulate sensation intensity, sensation type, and evoke sensations across a range of locations from the fingers to the upper arm using different stimulation electrodes even in an individual with chronic impairment of somatosensory function. These three features are essential to provide effective somatosensory feedback for neuroprosthetic applications.

Neurobionics and the brain-computer interface: current applications and future horizons

The brain-computer interface (BCI) is an exciting advance in neuroscience and engineering. In a motor BCI, electrical recordings from the motor cortex of paralysed humans are decoded by a computer and used to drive robotic arms or to restore movement in a paralysed hand by stimulating the muscles in the forearm. Simultaneously integrating a BCI with the sensory cortex will further enhance dexterity and fine control. BCIs are also being developed to: provide ambulation for paraplegic patients through controlling robotic exoskeletons; restore vision in people with acquired blindness; detect and control epileptic seizures; and improve control of movement disorders and memory enhancement. High-fidelity connectivity with small groups of neurons requires microelectrode placement in the cerebral cortex. Electrodes placed on the cortical surface are less invasive but produce inferior fidelity. Scalp surface recording using electroencephalography is much less precise. BCI technology is still in an early phase of development and awaits further technical improvements and larger multicentre clinical trials before wider clinical application and impact on the care of people with disabilities. There are also many ethical challenges to explore as this technology evolves.

Ultrasoft microwire neural electrodes improve chronic tissue integration

Chronically implanted neural multi-electrode arrays (MEA) are an essential technology for recording electrical signals from neurons and/or modulating neural activity through stimulation. However, current MEAs, regardless of the type, elicit an inflammatory response that ultimately leads to device failure. Traditionally, rigid materials like tungsten and silicon have been employed to interface with the relatively soft neural tissue. The large stiffness mismatch is thought to exacerbate the inflammatory response. In order to minimize the disparity between the device and the brain, we fabricated novel ultrasoft electrodes consisting of elastomers and conducting polymers with mechanical properties much more similar to those of brain tissue than previous neural implants. In this study, these ultrasoft microelectrodes were inserted and released using a stainless steel shuttle with polyethyleneglycol (PEG) glue. The implanted microwires showed functionality in acute neural stimulation. When implanted for 1 or 8weeks, the novel soft implants demonstrated significantly reduced inflammatory tissue response at week 8 compared to tungsten wires of similar dimension and surface chemistry. Furthermore, a higher degree of cell body distortion was found next to the tungsten implants compared to the polymer implants. Our results support the use of these novel ultrasoft electrodes for long term neural implants.

STATEMENT OF SIGNIFICANCE

One critical challenge to the translation of neural recording/stimulation electrode technology to clinically viable devices for brain computer interface (BCI) or deep brain stimulation (DBS) applications is the chronic degradation of device performance due to the inflammatory tissue reaction. While many hypothesize that soft and flexible devices elicit reduced inflammatory tissue responses, there has yet to be a rigorous comparison between soft and stiff implants. We have developed an ultra-soft microelectrode with Young's modulus lower than 1MPa, closely mimicking the brain tissue modulus. Here, we present a rigorous histological comparison of this novel ultrasoft electrode and conventional stiff electrode with the same size, shape and surface chemistry, implanted in rat brains for 1-week and 8-weeks. Significant improvement was observed for ultrasoft electrodes, including inflammatory tissue reaction, electrode-tissue integration as well as mechanical disturbance to nearby neurons. A full spectrum of new techniques were developed in this study, from insertion shuttle to in situ sectioning of the microelectrode to automated cell shape analysis, all of which should contribute new methods to the field. Finally, we showed the electrical functionality of the ultrasoft electrode, demonstrating the potential of flexible neural implant devices for future research and clinical use.

Informed consent in implantable BCI research: identification of research risks and recommendations for development of best practices

OBJECTIVE

Implantable brain-computer interface (BCI) research promises improvements in human health and enhancements in quality of life. Informed consent of subjects is a central tenet of this research. Rapid advances in neuroscience, and the intimate connection between functioning of the brain and conceptions of the self, make informed consent particularly challenging in BCI research. Identification of safety and research-related risks associated with BCI devices is an important step in ensuring meaningful informed consent.

APPROACH

This paper highlights a number of BCI research risks, including safety concerns, cognitive and communicative impairments, inappropriate subject expectations, group vulnerabilities, privacy and security, and disruptions of identity.

MAIN RESULTS

Based on identified BCI research risks, best practices are needed for understanding and incorporating BCI-related risks into informed consent protocols.

SIGNIFICANCE

Development of best practices should be guided by processes that are: multidisciplinary, systematic and transparent, iterative, relational and exploratory.

Flight simulation using a Brain-Computer Interface: A pilot, pilot study

As Brain-Computer Interface (BCI) systems advance for uses such as robotic arm control it is postulated that the control paradigms could apply to other scenarios, such as control of video games, wheelchair movement or even flight. The purpose of this pilot study was to determine whether our BCI system, which involves decoding the signals of two 96-microelectrode arrays implanted into the motor cortex of a subject, could also be used to control an aircraft in a flight simulator environment. The study involved six sessions in which various parameters were modified in order to achieve the best flight control, including plane type, view, control paradigm, gains, and limits. Successful flight was determined qualitatively by evaluating the subject's ability to perform requested maneuvers, maintain flight paths, and avoid control losses such as dives, spins and crashes. By the end of the study, it was found that the subject could successfully control an aircraft. The subject could use both the jet and propeller plane with different views, adopting an intuitive control paradigm. From the subject's perspective, this was one of the most exciting and entertaining experiments she had performed in two years of research. In conclusion, this study provides a proof-of-concept that traditional motor cortex signals combined with a decoding paradigm can be used to control systems besides a robotic arm for which the decoder was developed. Aside from possible functional benefits, it also shows the potential for a new recreational activity for individuals with disabilities who are able to master BCI control.

Soft implantable microelectrodes for future medicine: prosthetics, neural signal recording and neuromodulation

Implantable devices have provided various potential diagnostic options and therapeutic methods in diverse medical fields. A variety of hard-material-based implantable electrodes have been developed. However, several limitations for their chronic implantation remain, including mechanical mismatches at the interface between the electrode and the soft tissue, and biocompatibility. Soft-material-based implantable devices are suitable candidates for complementing the limitations of hard electrodes. Advances in microtechnology and materials science have largely solved many challenges, such as optimization of shape, minimization of infection, enhancement of biocompatibility and integration with components for diverse functions. Significant strides have also been made in mechanical matching of electrodes to soft tissue. In this review, we provide an overview of recent advances in soft-material-based implantable electrodes for medical applications, categorized according to their implantation site and material composition. We then review specific applications in three categories: neuroprosthetics, neural signal recording, and neuromodulation. Finally, we describe various strategies for the future development and application of implantable, soft-material-based devices.

Interfacing in silico and in vitro neuronal networks

In this study, an in vitro cortical culture was stimulated according to the activity exhibited by its in silico counterpart. We connected the latter, an artificial spiking neural network (SNN), to the former, a biological network (BNN), via an open-loop (i.e. unidirectional) configuration, with the ultimate goal of establishing a closed-loop bidirectional connection in future studies. We detected network bursts in the SNN and utilized them as triggers for stimulus delivery to the BNN. We analyzed evoked BNN responses to evaluate whether the BNN activity was entrained by the SNN by correlating stimuli and BNN evoked network burst rates at different timescales. We found that this correlation was nearly constant at all timescales, but its magnitude depended on the efficacy of the stimulation source in entraining BNN activity.

Data-driven model comparing the effects of glial scarring and interface interactions on chronic neural recordings in non-human primates

OBJECTIVE

We characterized electrode stability over twelve weeks of impedance and neural recording data from four chronically-implanted Utah arrays in two rhesus macaques, and investigated the effects of glial scarring and interface interactions at the electrode recording site on signal quality using a computational model.

APPROACH

A finite-element model of a Utah array microelectrode in neural tissue was coupled with a multi-compartmental model of a neuron to quantify the effects of encapsulation thickness, encapsulation resistivity, and interface resistivity on electrode impedance and waveform amplitude. The coupled model was then reconciled with the in vivo data. Histology was obtained seventeen weeks post-implantation to measure gliosis.

MAIN RESULTS

From week 1-3, mean impedance and amplitude increased at rates of 115.8 kΩ/week and 23.1 μV/week, respectively. This initial ramp up in impedance and amplitude was observed across all arrays, and is consistent with biofouling (increasing interface resistivity) and edema clearing (increasing tissue resistivity), respectively, in the model. Beyond week 3, the trends leveled out. Histology showed that thin scars formed around the electrodes. In the model, scarring could not match the in vivo data. However, a thin interface layer at the electrode tip could. Despite having a large effect on impedance, interface resistivity did not have a noticeable effect on amplitude.

SIGNIFICANCE

This study suggests that scarring does not cause an electrical problem with regard to signal quality since it does not appear to be the main contributor to increasing impedance or significantly affect amplitude unless it displaces neurons. This, in turn, suggests that neural signals can be obtained reliably despite scarring as long as the recording site has sufficiently low impedance after accumulating a thin layer of biofouling. Therefore, advancements in microelectrode technology may be expedited by focusing on improvements to the recording site-tissue interface rather than elimination of the glial scar.

Systems Neuroengineering: Understanding and Interacting with the Brain

In this paper, we review the current state-of-the-art techniques used for understanding the inner workings of the brain at a systems level. The neural activity that governs our everyday lives involves an intricate coordination of many processes that can be attributed to a variety of brain regions. On the surface, many of these functions can appear to be controlled by specific anatomical structures; however, in reality, numerous dynamic networks within the brain contribute to its function through an interconnected web of neuronal and synaptic pathways. The brain, in its healthy or pathological state, can therefore be best understood by taking a systems-level approach. While numerous neuroengineering technologies exist, we focus here on three major thrusts in the field of systems neuroengineering: neuroimaging, neural interfacing, and neuromodulation. Neuroimaging enables us to delineate the structural and functional organization of the brain, which is key in understanding how the neural system functions in both normal and disease states. Based on such knowledge, devices can be used either to communicate with the neural system, as in neural interface systems, or to modulate brain activity, as in neuromodulation systems. The consideration of these three fields is key to the development and application of neuro-devices. Feedback-based neuro-devices require the ability to sense neural activity (via a neuroimaging modality) through a neural interface (invasive or noninvasive) and ultimately to select a set of stimulation parameters in order to alter neural function via a neuromodulation modality. Systems neuroengineering refers to the use of engineering tools and technologies to image, decode, and modulate the brain in order to comprehend its functions and to repair its dysfunction. Interactions between these fields will help to shape the future of systems neuroengineering-to develop neurotechniques for enhancing the understanding of whole-brain function and dysfunction, and the management of neurological and mental disorders.

Brain computer interface learning for systems based on electrocorticography and intracortical microelectrode arrays

A brain-computer interface (BCI) system transforms neural activity into control signals for external devices in real time. A BCI user needs to learn to generate specific cortical activity patterns to control external devices effectively. We call this process BCI learning, and it often requires significant effort and time. Therefore, it is important to study this process and develop novel and efficient approaches to accelerate BCI learning. This article reviews major approaches that have been used for BCI learning, including computer-assisted learning, co-adaptive learning, operant conditioning, and sensory feedback. We focus on BCIs based on electrocorticography and intracortical microelectrode arrays for restoring motor function. This article also explores the possibility of brain modulation techniques in promoting BCI learning, such as electrical cortical stimulation, transcranial magnetic stimulation, and optogenetics. Furthermore, as proposed by recent BCI studies, we suggest that BCI learning is in many ways analogous to motor and cognitive skill learning, and therefore skill learning should be a useful metaphor to model BCI learning.

Restoration of vision in blind individuals using bionic devices: a review with a focus on cortical visual prostheses

The field of neurobionics offers hope to patients with sensory and motor impairment. Blindness is a common cause of major sensory loss, with an estimated 39 million people worldwide suffering from total blindness in 2010. Potential treatment options include bionic devices employing electrical stimulation of the visual pathways. Retinal stimulation can restore limited visual perception to patients with retinitis pigmentosa, however loss of retinal ganglion cells precludes this approach. The optic nerve, lateral geniculate nucleus and visual cortex provide alternative stimulation targets, with several research groups actively pursuing a cortically-based device capable of driving several hundred stimulating electrodes. While great progress has been made since the earliest works of Brindley and Dobelle in the 1960s and 1970s, significant clinical, surgical, psychophysical, neurophysiological, and engineering challenges remain to be overcome before a commercially-available cortical implant will be realized. Selection of candidate implant recipients will require assessment of their general, psychological and mental health, and likely responses to visual cortex stimulation. Implant functionality, longevity and safety may be enhanced by careful electrode insertion, optimization of electrical stimulation parameters and modification of immune responses to minimize or prevent the host response to the implanted electrodes. Psychophysical assessment will include mapping the positions of potentially several hundred phosphenes, which may require repetition if electrode performance deteriorates over time. Therefore, techniques for rapid psychophysical assessment are required, as are methods for objectively assessing the quality of life improvements obtained from the implant. These measures must take into account individual differences in image processing, phosphene distribution and rehabilitation programs that may be required to optimize implant functionality. In this review, we detail these and other challenges facing developers of cortical visual prostheses in addition to briefly outlining the epidemiology of blindness, and the history of cortical electrical stimulation in the context of visual prosthetics.

Performance measurement for brain-computer or brain-machine interfaces: a tutorial

OBJECTIVE

Brain-computer interfaces (BCIs) have the potential to be valuable clinical tools. However, the varied nature of BCIs, combined with the large number of laboratories participating in BCI research, makes uniform performance reporting difficult. To address this situation, we present a tutorial on performance measurement in BCI research.

APPROACH

A workshop on this topic was held at the 2013 International BCI Meeting at Asilomar Conference Center in Pacific Grove, California. This paper contains the consensus opinion of the workshop members, refined through discussion in the following months and the input of authors who were unable to attend the workshop.

MAIN RESULTS

Checklists for methods reporting were developed for both discrete and continuous BCIs. Relevant metrics are reviewed for different types of BCI research, with notes on their use to encourage uniform application between laboratories.

SIGNIFICANCE

Graduate students and other researchers new to BCI research may find this tutorial a helpful introduction to performance measurement in the field.