The neurochip BCI: towards a neural prosthesis for upper limb function

A spiking neural network with continuous local learning for robust online brain machine interface

Objective.Spiking neural networks (SNNs) are powerful tools that are well suited for brain machine interfaces (BMI) due to their similarity to biological neural systems and computational efficiency. They have shown comparable accuracy to state-of-the-art methods, but current training methods require large amounts of memory, and they cannot be trained on a continuous input stream without pausing periodically to perform backpropagation. An ideal BMI should be capable training continuously without interruption to minimize disruption to the user and adapt to changing neural environments.Approach.We propose a continuous SNN weight update algorithm that can be trained to perform regression learning with no need for storing past spiking events in memory. As a result, the amount of memory needed for training is constant regardless of the input duration. We evaluate the accuracy of the network on recordings of neural data taken from the premotor cortex of a primate performing reaching tasks. Additionally, we evaluate the SNN in a simulated closed loop environment and observe its ability to adapt to sudden changes in the input neural structure.Main results.The continuous learning SNN achieves the same peak correlation (ρ=0.7) as existing SNN training methods when trained offline on real neural data while reducing the total memory usage by 92%. Additionally, it matches state-of-the-art accuracy in a closed loop environment, demonstrates adaptability when subjected to multiple types of neural input disruptions, and is capable of being trained online without any prior offline training.Significance.This work presents a neural decoding algorithm that can be trained rapidly in a closed loop setting. The algorithm increases the speed of acclimating a new user to the system and also can adapt to sudden changes in neural behavior with minimal disruption to the user.

Implementation of artificial intelligence and machine learning-based methods in brain-computer interaction

Brain-computer interfaces are used for direct two-way communication between the human brain and the computer. Brain signals contain valuable information about the mental state and brain activity of the examined subject. However, due to their non-stationarity and susceptibility to various types of interference, their processing, analysis and interpretation are challenging. For these reasons, the research in the field of brain-computer interfaces is focused on the implementation of artificial intelligence, especially in five main areas: calibration, noise suppression, communication, mental condition estimation, and motor imagery. The use of algorithms based on artificial intelligence and machine learning has proven to be very promising in these application domains, especially due to their ability to predict and learn from previous experience. Therefore, their implementation within medical technologies can contribute to more accurate information about the mental state of subjects, alleviate the consequences of serious diseases or improve the quality of life of disabled patients.

Closed-Loop, Cervical, Epidural Stimulation Elicits Respiratory Neuroplasticity after Spinal Cord Injury in Freely Behaving Rats

Over half of all spinal cord injuries (SCIs) are cervical, which can lead to paralysis and respiratory compromise, causing significant morbidity and mortality. Effective treatments to restore breathing after severe upper cervical injury are lacking; thus, it is imperative to develop therapies to address this. Epidural stimulation has successfully restored motor function after SCI for stepping, standing, reaching, grasping, and postural control. We hypothesized that closed-loop stimulation triggered via healthy hemidiaphragm EMG activity has the potential to elicit functional neuroplasticity in spinal respiratory pathways after cervical SCI (cSCI). To test this, we delivered closed-loop, electrical, epidural stimulation (CLES) at the level of the phrenic motor nucleus (C4) for 3 d after C2 hemisection (C2HS) in freely behaving rats. A 2 × 2 Latin Square experimental design incorporated two treatments, C2HS injury and CLES therapy resulting in four groups of adult, female Sprague Dawley rats: C2HS + CLES (n = 8), C2HS (n = 6), intact + CLES (n = 6), intact (n = 6). In stimulated groups, CLES was delivered for 12-20 h/d for 3 d. After C2HS, 3 d of CLES robustly facilitated the slope of stimulus-response curves of ipsilesional spinal motor evoked potentials (sMEPs) versus nonstimulated controls. To our knowledge, this is the first demonstration of CLES eliciting respiratory neuroplasticity after C2HS in freely behaving animals. These findings suggest CLES as a promising future therapy to address respiratory deficiency associated with cSCI.

A Wireless Electro-Optic Platform for Multimodal Electrophysiology and Optogenetics in Freely Moving Rodents

This paper presents the design and the utilization of a wireless electro-optic platform to perform simultaneous multimodal electrophysiological recordings and optogenetic stimulation in freely moving rodents. The developed system can capture neural action potentials (AP), local field potentials (LFP) and electromyography (EMG) signals with up to 32 channels in parallel while providing four optical stimulation channels. The platform is using commercial off-the-shelf components (COTS) and a low-power digital field-programmable gate array (FPGA), to perform digital signal processing to digitally separate in real time the AP, LFP and EMG while performing signal detection and compression for mitigating wireless bandwidth and power consumption limitations. The different signal modalities collected on the 32 channels are time-multiplexed into a single data stream to decrease power consumption and optimize resource utilization. The data reduction strategy is based on signal processing and real-time data compression. Digital filtering, signal detection, and wavelet data compression are used inside the platform to separate the different electrophysiological signal modalities, namely the local field potentials (1–500 Hz), EMG (30–500 Hz), and the action potentials (300–5,000 Hz) and perform data reduction before transmitting the data. The platform achieves a measured data reduction ratio of 7.77 (for a firing rate of 50 AP/second) and weights 4.7 g with a 100-mAh battery, an on/off switch and a protective plastic enclosure. To validate the performance of the platform, we measured distinct electrophysiology signals and performed optogenetics stimulation in vivo in freely moving rondents. We recorded AP and LFP signals with the platform using a 16-microelectrode array implanted in the primary motor cortex of a Long Evans rat, both in anesthetized and freely moving conditions. EMG responses to optogenetic Channelrhodopsin-2 induced activation of motor cortex via optical fiber were also recorded in freely moving rodents.

A Closed-Loop Optogenetic Platform

Neuromodulation is an established treatment for numerous neurological conditions, but to expand the therapeutic scope there is a need to improve the spatial, temporal and cell-type specificity of stimulation. Optogenetics is a promising area of current research, enabling optical stimulation of genetically-defined cell types without interfering with concurrent electrical recording for closed-loop control of neural activity. We are developing an open-source system to provide a platform for closed-loop optogenetic neuromodulation, incorporating custom integrated circuitry for recording and stimulation, real-time closed-loop algorithms running on a microcontroller and experimental control via a PC interface. We include commercial components to validate performance, with the ultimate aim of translating this approach to humans. In the meantime our system is flexible and expandable for use in a variety of preclinical neuroscientific applications. The platform consists of a Controlling Abnormal Network Dynamics using Optogenetics (CANDO) Control System (CS) that interfaces with up to four CANDO headstages responsible for electrical recording and optical stimulation through custom CANDO LED optrodes. Control of the hardware, inbuilt algorithms and data acquisition is enabled via the CANDO GUI (Graphical User Interface). Here we describe the design and implementation of this system, and demonstrate how it can be used to modulate neuronal oscillations in vitro and in vivo.

A markerless platform for ambulatory systems neuroscience

Motor systems neuroscience seeks to understand how the brain controls movement. To minimize confounding variables, large-animal studies typically constrain body movement from areas not under observation, ensuring consistent, repeatable behaviors. Such studies have fueled decades of research, but they may be artificially limiting the richness of neural data observed, preventing generalization to more natural movements and settings. Neuroscience studies of unconstrained movement would capture a greater range of behavior and a more complete view of neuronal activity, but instrumenting an experimental rig suitable for large animals presents substantial engineering challenges. Here, we present a markerless, full-body motion tracking and synchronized wireless neural electrophysiology platform for large, ambulatory animals. Composed of four depth (RGB-D) cameras that provide a 360° view of a 4.5-square-meters enclosed area, this system is designed to record a diverse range of neuroethologically relevant behaviors. This platform also allows for the simultaneous acquisition of hundreds of wireless neural recording channels in multiple brain regions. As behavioral and neuronal data are generated at rates below 200 megabytes per second, a single desktop can facilitate hours of continuous recording. This setup is designed for systems neuroscience and neuroengineering research, where synchronized kinematic behavior and neural data are the foundation for investigation. By enabling the study of previously unexplored movement tasks, this system can generate insights into the functioning of the mammalian motor system and provide a platform to develop brain-machine interfaces for unconstrained applications.

Neurochip3: An Autonomous Multichannel Bidirectional Brain-Computer Interface for Closed-Loop Activity-Dependent Stimulation

Toward addressing many neuroprosthetic applications, the Neurochip3 (NC3) is a multichannel bidirectional brain-computer interface that operates autonomously and can support closed-loop activity-dependent stimulation. It consists of four circuit boards populated with off-the-shelf components and is sufficiently compact to be carried on the head of a non-human primate (NHP). NC3 has six main components: (1) an analog front-end with an Intan biophysical signal amplifier (16 differential or 32 single-ended channels) and a 3-axis accelerometer, (2) a digital control system comprised of a Cyclone V FPGA and Atmel SAM4 MCU, (3) a micro SD Card for 128 GB or more storage, (4) a 6-channel differential stimulator with ±60 V compliance, (5) a rechargeable battery pack supporting autonomous operation for up to 24 h and, (6) infrared transceiver and serial ports for communication. The NC3 and earlier versions have been successfully deployed in many closed-loop operations to induce synaptic plasticity and bridge lost biological connections, as well as deliver activity-dependent intracranial reinforcement. These paradigms to strengthen or replace impaired connections have many applications in neuroprosthetics and neurorehabilitation.

Autonomous cage-side system for remote training of non-human primates

BACKGROUND

Training non-human primates (NHPs) for translational medical experimentation is an essential yet time consuming process. To increase training efficiency, some training systems have been designed for NHPs to use at their home cages. Several autonomous cage-side tablet-based systems have been proposed, but none of these systems allow for remote monitoring and task modification while also being wireless, low-cost, light weight, and portable.

NEW METHOD

Here we present ACTS: an Autonomous Cage-side Training System which meets all these criteria. ACTS consists of 1) a touchscreen tablet and a speaker attached to the subject's home cage, 2) an inexpensive reward system made from a slightly modified fish feeder, and 3), a laptop operating the system wirelessly and remotely via a router.

RESULTS

We were able to test the system and wirelessly train two macaques in their home cages. Remote access enabled us to control ACTS from up to 90 m, through up to 3 walls, and through a floor of a building. The device is compatible with different reward pellet sizes and could run about two hours with a ∼4 mm pellet size. The animals were able to generalize the task when transferred to a traditional experimental rig.

COMPARISON WITH EXISTING METHODS

The low cost and modest skill required to build and implement ACTS lowers the barrier for NHP researchers and caregivers to deploy autonomous, remotely controlled tablet-based cage-side systems.

CONCLUSION

ACTS can be used for low-cost, wireless cage-side training of NHPs being prepared for translational medical experimentation.

Closed-Loop Neuromodulation in Physiological and Translational Research

Neuromodulation, the focused delivery of energy to neural tissue to affect neural or physiological processes, is a common method to study the physiology of the nervous system. It is also successfully used as treatment for disorders in which the nervous system is affected or implicated. Typically, neurostimulation is delivered in open-loop mode (i.e., according to a predetermined schedule and independently of the state of the organ or physiological system whose function is sought to be modulated). However, the physiology of the nervous system or the modulated organ can be dynamic, and the same stimulus may have different effects depending on the underlying state. As a result, open-loop stimulation may fail to restore the desired function or cause side effects. In such cases, a neuromodulation intervention may be preferable to be administered in closed-loop mode. In a closed-loop neuromodulation (CLN) system, stimulation is delivered when certain physiological states or conditions are met (responsive neurostimulation); the stimulation parameters can also be adjusted dynamically to optimize the effect of stimulation in real time (adaptive neurostimulation). In this review, the reasons and the conditions for using CLN are discussed, the basic components of a CLN system are described, and examples of CLN systems used in physiological and translational research are presented.

Locomotion Control of Pigeons using Polymer-based Deep Brain Electrodes

This paper describes the electrical modulation of locomotion in pigeons using deep brain electrodes. Polymer-based depth electrodes with four channels were fabricated. Based on the location of the nucleus intercollicularis (ICo), the shanks of the depth electrodes were designed to be a length of 11 mm. After the implantation of the depth electrode into the ICo region of the brain, it was connected by wires to a custom-made stimulator, and biphasic current pulses were delivered. Current pulses with an amplitude of 0.5 mA, a rate of 58.0 Hz, and a duration of $320\mu \mathrm{s} $s were applied for 0.5 s. When the ICo region was electrically stimulated, taking-off behavior was successfully induced for 0.4 s. Induction of taking-off behavior by electrical stimulation, when coupled to control of turning and running forward locomotions, may contribute to the development of remote flight-control system of freely moving pigeon.

Development of an Activity-Dependent Epidural Stimulation System in Freely Moving Spinal Cord Injured Rats: A Proof of Concept Study

Purpose: Extensive pre-clinical and clinical experimentation has yielded data on the robustness and versatility of epidural stimulation (ES) strategies to activate spinal neural circuitry to produce functional benefits. Increasing studies are now reporting that closed-loop electrical stimulation delivery methods significantly enhance the neuromodulation effects of stimulation, to in turn, improve physiological outcomes of the intervention. No studies have yet explored the feasibility and usage of closed-loop systems to neuromodulate the cervical spinal cord using ES. Methods: We developed an activity-dependent system that utilizes electromyography (EMG) activity to trigger epidural stimulation (tES) of the cervical spinal cord in awake, freely moving rats. Experiments were performed on rats that were implanted with chronic forelimb EMG and cervical epidural implants, with (n = 7) and without (n = 2) a complete C4 spinal hemisection. Results: Our results show that the EMG triggered activity-dependent system can be reliably applied and reproduced for: (i) stimulating multiple rats simultaneously throughout the night during free home-cage activity and (ii) use as a mobile system for testing and training during various short-term behavioral testing conditions. The system was able to consistently generate stimulation pulse trains in response to attempted EMG activity that crossed a user-defined threshold in all rats for all experiments, including the overnight experiments that lasts for 7 h/session for 6 days/week through the 3-month period. Conclusion: The developed closed-loop system can be considered to represent a class of bidirectional neural prostheses via a circuit that enables two-way interactions between neural activity (real-time processing of EMG activity) and external devices (such as a stimulator). It can operate autonomously for extended periods of time in unrestrained rats, allowing its use as a long-term therapeutic tool. It can also enable us to study the long-term physiological effects of incorporating electrical stimulation techniques into the nervous system. The system can also be experimented for connecting several neural systems into a Brainet by combining neural signals from multiple rats dynamically and in real-time so as to enhance motor performance. Studies are ongoing in our laboratory to test the usefulness of this system in the recovery of hand function after cervical spinal cord injuries.

Neuromorphic neural interfaces: from neurophysiological inspiration to biohybrid coupling with nervous systems

OBJECTIVE

Computation in nervous systems operates with different computational primitives, and on different hardware, than traditional digital computation and is thus subjected to different constraints from its digital counterpart regarding the use of physical resources such as time, space and energy. In an effort to better understand neural computation on a physical medium with similar spatiotemporal and energetic constraints, the field of neuromorphic engineering aims to design and implement electronic systems that emulate in very large-scale integration (VLSI) hardware the organization and functions of neural systems at multiple levels of biological organization, from individual neurons up to large circuits and networks. Mixed analog/digital neuromorphic VLSI systems are compact, consume little power and operate in real time independently of the size and complexity of the model.

APPROACH

This article highlights the current efforts to interface neuromorphic systems with neural systems at multiple levels of biological organization, from the synaptic to the system level, and discusses the prospects for future biohybrid systems with neuromorphic circuits of greater complexity.

MAIN RESULTS

Single silicon neurons have been interfaced successfully with invertebrate and vertebrate neural networks. This approach allowed the investigation of neural properties that are inaccessible with traditional techniques while providing a realistic biological context not achievable with traditional numerical modeling methods. At the network level, populations of neurons are envisioned to communicate bidirectionally with neuromorphic processors of hundreds or thousands of silicon neurons. Recent work on brain-machine interfaces suggests that this is feasible with current neuromorphic technology.

SIGNIFICANCE

Biohybrid interfaces between biological neurons and VLSI neuromorphic systems of varying complexity have started to emerge in the literature. Primarily intended as a computational tool for investigating fundamental questions related to neural dynamics, the sophistication of current neuromorphic systems now allows direct interfaces with large neuronal networks and circuits, resulting in potentially interesting clinical applications for neuroengineering systems, neuroprosthetics and neurorehabilitation.

Open-Source, Low Cost, Free-Behavior Monitoring, and Reward System for Neuroscience Research in Non-human Primates

We describe a low-cost system designed to document bodily movement and neural activity and deliver rewards to monkeys behaving freely in their home cage. An important application is to studying brain-machine interface (BMI) systems during free behavior, since brain signals associated with natural movement can differ significantly from those associated with more commonly used constrained conditions. Our approach allows for short-latency (<500 ms) reward delivery and behavior monitoring using low-cost off-the-shelf components. This system interfaces existing untethered recording equipment with a custom hub that controls a cage-mounted feeder. The behavior monitoring system uses a depth camera to provide real-time, easy-to-analyze, gross movement data streams. In a proof-of-concept experiment we demonstrate robust learning of neural activity using the system over 14 behavioral sessions.

Decoding Local Field Potentials for Neural Interfaces

The stability and frequency content of local field potentials (LFPs) offer key advantages for long-term, low-power neural interfaces. However, interpreting LFPs may require new signal processing techniques which should be informed by a scientific understanding of how these recordings arise from the coordinated activity of underlying neuronal populations. We review current approaches to decoding LFPs for brain-machine interface (BMI) applications, and suggest several directions for future research. To facilitate an improved understanding of the relationship between LFPs and spike activity, we share a dataset of multielectrode recordings from monkey motor cortex, and describe two unsupervised analysis methods we have explored for extracting a low-dimensional feature space that is amenable to biomimetic decoding and biofeedback training.

The Importance of Visual Feedback Design in BCIs; from Embodiment to Motor Imagery Learning

Brain computer interfaces (BCIs) have been developed and implemented in many areas as a new communication channel between the human brain and external devices. Despite their rapid growth and broad popularity, the inaccurate performance and cost of user-training are yet the main issues that prevent their application out of the research and clinical environment. We previously introduced a BCI system for the control of a very humanlike android that could raise a sense of embodiment and agency in the operators only by imagining a movement (motor imagery) and watching the robot perform it. Also using the same setup, we further discovered that the positive bias of subjects' performance both increased their sensation of embodiment and improved their motor imagery skills in a short period. In this work, we studied the shared mechanism between the experience of embodiment and motor imagery. We compared the trend of motor imagery learning when two groups of subjects BCI-operated different looking robots, a very humanlike android's hands and a pair of metallic gripper. Although our experiments did not show a significant change of learning between the two groups immediately during one session, the android group revealed better motor imagery skills in the follow up session when both groups repeated the task using the non-humanlike gripper. This result shows that motor imagery skills learnt during the BCI-operation of humanlike hands are more robust to time and visual feedback changes. We discuss the role of embodiment and mirror neuron system in such outcome and propose the application of androids for efficient BCI training.

Towards a miniaturized brain-machine-spinal cord interface (BMSI) for restoration of function after spinal cord injury

Nearly 6 million people in the United States are currently living with paralysis in which 23% of the cases are related to spinal cord injury (SCI). Miniaturized closed-loop neural interfaces have the potential for restoring function and mobility lost to debilitating neural injuries such as SCI by leveraging recent advancements in bioelectronics and a better understanding of the processes that underlie functional and anatomical reorganization in an injured nervous system. This paper describes our current progress towards developing a miniaturized brain-machine-spinal cord interface (BMSI) that is envisioned to convert in real time the neural command signals recorded from the brain to electrical stimuli delivered to the spinal cord below the injury level. Specifically, the paper reports on a corticospinal interface integrated circuit (IC) as a core building block for such a BMSI that is capable of low-noise recording of extracellular neural spikes from the cerebral cortex as well as muscle activation using intraspinal microstimulation (ISMS) in a rat with contusion injury to the thoracic spinal cord. The paper further presents results from a neurobiological study conducted in both normal and SCI rats to investigate the effect of various ISMS parameters on movement thresholds in the rat hindlimb. Coupled with proper signal-processing algorithms in the future for the transformation between the cortically recorded data and ISMS parameters, such a BMSI has the potential to facilitate functional recovery after an SCI by re-establishing corticospinal communication channels lost due to the injury.

Miniaturized Technologies for Enhancement of Motor Plasticity

The idea that the damaged brain can functionally reorganize itself – so when one part fails, there lies the possibility for another to substitute – is an exciting discovery of the twentieth century. We now know that motor circuits once presumed to be hardwired are not, and motor-skill learning, exercise, and even mental rehearsal of motor tasks can turn genes on or off to shape brain architecture, function, and, consequently, behavior. This is a very significant alteration from our previously static view of the brain and has profound implications for the rescue of function after a motor injury. Presentation of the right cues, applied in relevant spatiotemporal geometries, is required to awaken the dormant plastic forces essential for repair. The focus of this review is to highlight some of the recent progress in neural interfaces designed to harness motor plasticity, and the role of miniaturization in development of strategies that engage diverse elements of the neuronal machinery to synergistically facilitate recovery of function after motor damage.

A cortical-spinal prosthesis for targeted limb movement in paralysed primate avatars

Motor paralysis is among the most disabling aspects of injury to the central nervous system. Here we develop and test a target-based cortical-spinal neural prosthesis that employs neural activity recorded from pre-motor neurons to control limb movements in functionally paralyzed primate avatars. Given the complexity by which muscle contractions are naturally controlled, we approach the problem of eliciting goal-directed limb movement in paralyzed animals by focusing on the intended targets of movement rather than their intermediate trajectories. We then match this information in real-time with spinal cord and muscle stimulation parameters that produce free planar limb movements to those intended target locations. We demonstrate that both the decoded activities of pre-motor populations and their adaptive responses can be used, after brief training, to effectively direct an avatar’s limb to distinct targets variably displayed on a screen. These findings advance the future possibility of reconstituting targeted limb movement in paralyzed subjects.

Volitional walking via upper limb muscle-controlled stimulation of the lumbar locomotor center in man

Gait disturbance in individuals with spinal cord lesion is attributed to the interruption of descending pathways to the spinal locomotor center, whereas neural circuits below and above the lesion maintain their functional capability. An artificial neural connection (ANC), which bridges supraspinal centers and locomotor networks in the lumbar spinal cord beyond the lesion site, may restore the functional impairment. To achieve an ANC that sends descending voluntary commands to the lumbar locomotor center and bypasses the thoracic spinal cord, upper limb muscle activity was converted to magnetic stimuli delivered noninvasively over the lumbar vertebra. Healthy participants were able to initiate and terminate walking-like behavior and to control the step cycle through an ANC controlled by volitional upper limb muscle activity. The walking-like behavior stopped just after the ANC was disconnected from the participants even when the participant continued to swing arms. Furthermore, additional simultaneous peripheral electrical stimulation to the foot via the ANC enhanced this walking-like behavior. Kinematics of the induced behaviors were identical to those observed in voluntary walking. These results demonstrate that the ANC induces volitionally controlled, walking-like behavior of the legs. This paradigm may be able to compensate for the dysfunction of descending pathways by sending commands to the preserved locomotor center at the lumbar spinal cord and may enable individuals with paraplegia to regain volitionally controlled walking.

Joint cross-correlation analysis reveals complex, time-dependent functional relationship between cortical neurons and arm electromyograms

Correlation between cortical activity and electromyographic (EMG) activity of limb muscles has long been a subject of neurophysiological studies, especially in terms of corticospinal connectivity. Interest in this issue has recently increased due to the development of brain-machine interfaces with output signals that mimic muscle force. For this study, three monkeys were implanted with multielectrode arrays in multiple cortical areas. One monkey performed self-timed touch pad presses, whereas the other two executed arm reaching movements. We analyzed the dynamic relationship between cortical neuronal activity and arm EMGs using a joint cross-correlation (JCC) analysis that evaluated trial-by-trial correlation as a function of time intervals within a trial. JCCs revealed transient correlations between the EMGs of multiple muscles and neural activity in motor, premotor and somatosensory cortical areas. Matching results were obtained using spike-triggered averages corrected by subtracting trial-shuffled data. Compared with spike-triggered averages, JCCs more readily revealed dynamic changes in cortico-EMG correlations. JCCs showed that correlation peaks often sharpened around movement times and broadened during delay intervals. Furthermore, JCC patterns were directionally selective for the arm-reaching task. We propose that such highly dynamic, task-dependent and distributed relationships between cortical activity and EMGs should be taken into consideration for future brain-machine interfaces that generate EMG-like signals.

Electronic bypass of spinal lesions: activation of lower motor neurons directly driven by cortical neural signals

Background

Lower motor neurons in the spinal cord lose supraspinal inputs after complete spinal cord injury, leading to a loss of volitional control below the injury site. Extensive locomotor training with spinal cord stimulation can restore locomotion function after spinal cord injury in humans and animals. However, this locomotion is non-voluntary, meaning that subjects cannot control stimulation via their natural “intent”. A recent study demonstrated an advanced system that triggers a stimulator using forelimb stepping electromyographic patterns to restore quadrupedal walking in rats with spinal cord transection. However, this indirect source of “intent” may mean that other non-stepping forelimb activities may false-trigger the spinal stimulator and thus produce unwanted hindlimb movements.

Methods

We hypothesized that there are distinguishable neural activities in the primary motor cortex during treadmill walking, even after low-thoracic spinal transection in adult guinea pigs. We developed an electronic spinal bridge, called “Motolink”, which detects these neural patterns and triggers a “spinal” stimulator for hindlimb movement. This hardware can be head-mounted or carried in a backpack. Neural data were processed in real-time and transmitted to a computer for analysis by an embedded processor. Off-line neural spike analysis was conducted to calculate and preset the spike threshold for “Motolink” hardware.

Results

We identified correlated activities of primary motor cortex neurons during treadmill walking of guinea pigs with spinal cord transection. These neural activities were used to predict the kinematic states of the animals. The appropriate selection of spike threshold value enabled the “Motolink” system to detect the neural “intent” of walking, which triggered electrical stimulation of the spinal cord and induced stepping-like hindlimb movements.

Conclusion

We present a direct cortical “intent”-driven electronic spinal bridge to restore hindlimb locomotion after complete spinal cord injury.

Preliminary investigation of an electromyography-controlled video game as a home program for persons in the chronic phase of stroke recovery

OBJECTIVE

To investigate the preliminary effectiveness of surface electromyography (sEMG) biofeedback delivered via interaction with a commercial computer game to improve motor control in chronic stroke survivors.

DESIGN

Single-blinded, 1-group, repeated-measures design: A1, A2, B, A3 (A, assessment; B, intervention).

SETTING

Laboratory and participants' homes.

PARTICIPANTS

A convenience sample of persons (N=9) between 40 and 75 years of age with moderate to severe upper extremity motor impairment and at least 6 months poststroke completed the study.

INTERVENTION

The electromyography-controlled video game system targeted the wrist muscle activation with the goal of increasing selective muscle activation. Participants received several laboratory training sessions with the system and then were instructed to use the system at home for 45 minutes, 5 times per week for the following 4 weeks.

MAIN OUTCOME MEASURES

Primary outcome measures included duration of system use, sEMG during home play, and pre/post sEMG measures during active wrist motion. Secondary outcomes included kinematic analysis of movement and functional outcomes, including the Wolf Motor Function Test and the Chedoke Arm and Hand Activity Inventory-9.

RESULTS

One third of participants completed or exceeded the recommended amount of system use. Statistically significant changes were observed on both game play and pre/post sEMG outcomes. Limited carryover, however, was observed on kinematic or functional outcomes.

CONCLUSIONS

This preliminary investigation indicates that use of the electromyography-controlled video game impacts muscle activation. Limited changes in kinematic and activity level outcomes, however, suggest that the intervention may benefit from the inclusion of a functional activity component.

Spike-timing-dependent plasticity in primate corticospinal connections induced during free behavior

Motor learning and functional recovery from brain damage involve changes in the strength of synaptic connections between neurons. Relevant in vivo evidence on the underlying cellular mechanisms remains limited and indirect. We found that the strength of neural connections between motor cortex and spinal cord in monkeys can be modified with an autonomous recurrent neural interface that delivers electrical stimuli in the spinal cord triggered by action potentials of corticospinal cells during free behavior. The activity-dependent stimulation modified the strength of the terminal connections of single corticomotoneuronal cells, consistent with a bidirectional spike-timing-dependent plasticity rule previously derived from in vitro experiments. For some cells, the changes lasted for days after the end of conditioning, but most effects eventually reverted to preconditioning levels. These results provide direct evidence of corticospinal synaptic plasticity in vivo at the level of single neurons induced by normal firing patterns during free behavior.

NeuroGame Therapy to improve wrist control in children with cerebral palsy: a case series

OBJECTIVE

This case series examines the feasibility, specificity, and preliminary effectiveness of NeuroGame Therapy (NGT) for improving wrist control in four children with cerebral palsy (CP). NGT uses surface electromyographic (sEMG) signals routed through motivating computer games to improve motor control.

METHODS

Primary outcomes of NGT included feasibility (hours of play) and specificity (changes in sEMG activity during game play). Secondary outcomes included changes in co-contraction, range of motion, segmental alignment, and spontaneous upper extremity function following intervention.

RESULTS

Participants completed a mean of 8.8 hours of NGT over 5-6 weeks. Participants demonstrated dramatic improvement of the sEMG activity during game play. Several participants also showed improvements in range of motion, co-contraction, and spontaneous upper extremity function following NGT.

CONCLUSION

This case series provides evidence for the feasibility, specificity, and effectiveness of NGT. Future studies will pair NGT with functional practice to improve transfer of learning to daily activities.

Fabrication of polydimethylsiloxane (PDMS) - based multielectrode array for neural interface

Flexible multielectrode arrays (MEAs) are being developed with various materials, and polyimide has been widely used due to the conveniece of process. Polyimide is developed in the form of photoresist. And this enable precise and reproducible fabrication. PDMS is another good candidate for MEA base material, but it has poor surface energy and etching property. In this paper, we proposed a better fabrication process that could modify PDMS surface for a long time and open the site of electrode and pad efficiently without PDMS etching.

Myo-cortical crossed feedback reorganizes primate motor cortex output

The motor system is capable of adapting to changed conditions such as amputations or lesions by reorganizing cortical representations of peripheral musculature. To investigate the underlying mechanisms we induced targeted reorganization of motor output effects by establishing an artificial recurrent connection between a forelimb muscle and an unrelated site in the primary motor cortex (M1) of macaques. A head-fixed computer transformed forelimb electromyographic activity into proportional subthreshold intracortical microstimulation (ICMS) during hours of unrestrained volitional behavior. This conditioning paradigm stimulated the cortical site for a particular muscle in proportion to activation of another muscle and induced robust site- and input-specific reorganization of M1 output effects. Reorganization was observed within 25 min and could be maintained with intermittent conditioning for successive days. Control stimulation that was independent of muscle activity, termed "pseudoconditioning," failed to produce reorganization. Preconditioning output effects were gradually restored during volitional behaviors following the end of conditioning. The ease of changing the relationship between cortical sites and associated muscle responses suggests that under normal conditions these relations are maintained through physiological feedback loops. These findings demonstrate that motor cortex outputs may be reorganized in a targeted and sustainable manner through artificial afferent feedback triggered from controllable and readily recorded muscle activity. Such cortical reorganization has implications for therapeutic treatment of neurological injuries.

Restoration of upper limb movement via artificial corticospinal and musculospinal connections in a monkey with spinal cord injury

Functional loss of limb control in individuals with spinal cord injury or stroke can be caused by interruption of corticospinal pathways, although the neural circuits located above and below the lesion remain functional. An artificial neural connection that bridges the lost pathway and connects cortical to spinal circuits has potential to ameliorate the functional loss. We investigated the effects of introducing novel artificial neural connections in a paretic monkey that had a unilateral spinal cord lesion at the C2 level. The first application bridged the impaired spinal lesion. This allowed the monkey to drive the spinal stimulation through volitionally controlled power of high-gamma activity in either the premotor or motor cortex, and thereby to acquire a force-matching target. The second application created an artificial recurrent connection from a paretic agonist muscle to a spinal site, allowing muscle-controlled spinal stimulation to boost on-going activity in the muscle. These results suggest that artificial neural connections can compensate for interrupted descending pathways and promote volitional control of upper limb movement after damage of descending pathways such as spinal cord injury or stroke.

Quantitative evaluation of muscle synergy models: a single-trial task decoding approach

Muscle synergies, i.e., invariant coordinated activations of groups of muscles, have been proposed as building blocks that the central nervous system (CNS) uses to construct the patterns of muscle activity utilized for executing movements. Several efficient dimensionality reduction algorithms that extract putative synergies from electromyographic (EMG) signals have been developed. Typically, the quality of synergy decompositions is assessed by computing the Variance Accounted For (VAF). Yet, little is known about the extent to which the combination of those synergies encodes task-discriminating variations of muscle activity in individual trials. To address this question, here we conceive and develop a novel computational framework to evaluate muscle synergy decompositions in task space. Unlike previous methods considering the total variance of muscle patterns (VAF based metrics), our approach focuses on variance discriminating execution of different tasks. The procedure is based on single-trial task decoding from muscle synergy activation features. The task decoding based metric evaluates quantitatively the mapping between synergy recruitment and task identification and automatically determines the minimal number of synergies that captures all the task-discriminating variability in the synergy activations. In this paper, we first validate the method on plausibly simulated EMG datasets. We then show that it can be applied to different types of muscle synergy decomposition and illustrate its applicability to real data by using it for the analysis of EMG recordings during an arm pointing task. We find that time-varying and synchronous synergies with similar number of parameters are equally efficient in task decoding, suggesting that in this experimental paradigm they are equally valid representations of muscle synergies. Overall, these findings stress the effectiveness of the decoding metric in systematically assessing muscle synergy decompositions in task space.

Closed-Loop, Multichannel Experimentation Using the Open-Source NeuroRighter Electrophysiology Platform

Single neuron feedback control techniques, such as voltage clamp and dynamic clamp, have enabled numerous advances in our understanding of ion channels, electrochemical signaling, and neural dynamics. Although commercially available multichannel recording and stimulation systems are commonly used for studying neural processing at the network level, they provide little native support for real-time feedback. We developed the open-source NeuroRighter multichannel electrophysiology hardware and software platform for closed-loop multichannel control with a focus on accessibility and low cost. NeuroRighter allows 64 channels of stimulation and recording for around US $10,000, along with the ability to integrate with other software and hardware. Here, we present substantial enhancements to the NeuroRighter platform, including a redesigned desktop application, a new stimulation subsystem allowing arbitrary stimulation patterns, low-latency data servers for accessing data streams, and a new application programming interface (API) for creating closed-loop protocols that can be inserted into NeuroRighter as plugin programs. This greatly simplifies the design of sophisticated real-time experiments without sacrificing the power and speed of a compiled programming language. Here we present a detailed description of NeuroRighter as a stand-alone application, its plugin API, and an extensive set of case studies that highlight the system’s abilities for conducting closed-loop, multichannel interfacing experiments.

Sub-millisecond closed-loop feedback stimulation between arbitrary sets of individual neurons

We present a system to artificially correlate the spike timing between sets of arbitrary neurons that were interfaced to a complementary metal–oxide–semiconductor (CMOS) high-density microelectrode array (MEA). The system features a novel reprogrammable and flexible event engine unit to detect arbitrary spatio-temporal patterns of recorded action potentials and is capable of delivering sub-millisecond closed-loop feedback of electrical stimulation upon trigger events in real-time. The relative timing between action potentials of individual neurons as well as the temporal pattern among multiple neurons, or neuronal assemblies, is considered an important factor governing memory and learning in the brain. Artificially changing timings between arbitrary sets of spiking neurons with our system could provide a “knob” to tune information processing in the network.

Neural interfaces for the brain and spinal cord--restoring motor function

Regaining motor function is of high priority to patients with spinal cord injury (SCI). A variety of electronic devices that interface with the brain or spinal cord, which have applications in neural prosthetics and neurorehabilitation, are in development. Owing to our advancing understanding of activity-dependent synaptic plasticity, new technologies to monitor, decode and manipulate neural activity are being translated to patient populations, and have demonstrated clinical efficacy. Brain-machine interfaces that decode motor intentions from cortical signals are enabling patient-driven control of assistive devices such as computers and robotic prostheses, whereas electrical stimulation of the spinal cord and muscles can aid in retraining of motor circuits and improve residual capabilities in patients with SCI. Next-generation interfaces that combine recording and stimulating capabilities in so-called closed-loop devices will further extend the potential for neuroelectronic augmentation of injured motor circuits. Emerging evidence suggests that integration of closed-loop interfaces into intentional motor behaviours has therapeutic benefits that outlast the use of these devices as prostheses. In this Review, we summarize this evidence and propose that several known plasticity mechanisms, operating in a complementary manner, might underlie the therapeutic effects that are achieved by closing the loop between electronic devices and the nervous system.

P300 brain computer interface: current challenges and emerging trends

A brain-computer interface (BCI) enables communication without movement based on brain signals measured with electroencephalography (EEG). BCIs usually rely on one of three types of signals: the P300 and other components of the event-related potential (ERP), steady state visual evoked potential (SSVEP), or event related desynchronization (ERD). Although P300 BCIs were introduced over twenty years ago, the past few years have seen a strong increase in P300 BCI research. This closed-loop BCI approach relies on the P300 and other components of the ERP, based on an oddball paradigm presented to the subject. In this paper, we overview the current status of P300 BCI technology, and then discuss new directions: paradigms for eliciting P300s; signal processing methods; applications; and hybrid BCIs. We conclude that P300 BCIs are quite promising, as several emerging directions have not yet been fully explored and could lead to improvements in bit rate, reliability, usability, and flexibility.

Reanimating the arm and hand with intraspinal microstimulation

To date, there is no effective therapy for spinal cord injury, and many patients could benefit dramatically from at least partial restoration of arm and hand function. Despite a substantial body of research investigating intraspinal microstimulation (ISMS) in frogs, rodents and cats, little is known about upper-limb responses to cervical stimulation in the primate. Here, we show for the first time that long trains of ISMS delivered to the macaque spinal cord can evoke functional arm and hand movements. Complex movements involving coordinated activation of multiple muscles could be elicited from a single electrode, while just two electrodes were required for independent control of reaching and grasping. We found that the motor responses to ISMS were described by a dual exponential model that depended only on stimulation history. We demonstrate that this model can be inverted to generate stimulus trains capable of eliciting arbitrary, graded motor responses, and could be used to restore volitional movements in a closed-loop brain-machine interface.

A miniaturized system for spike-triggered intracortical microstimulation in an ambulatory rat

This paper reports on a miniaturized system for spike-triggered intracortical microstimulation (ICMS) in an ambulatory rat. The head-mounted microdevice comprises a previously developed application-specific integrated circuit fabricated in 0.35-μm two-poly four-metal complementary metal-oxide-semiconductor technology, which is assembled and packaged on a miniature rigid-flex substrate together with a few external components for programming, supply regulation, and wireless operation. The microdevice operates autonomously from a single 1.55-V battery, measures 3.6  cm × 1.3  cm × 0.6  cm, weighs 1.7  g (including the battery), and is capable of stimulating as well as recording the neural response to ICMS in biological experiments with anesthetized laboratory rats. Moreover, it has been interfaced with silicon microelectrodes chronically implanted in the cerebral cortex of an ambulatory rat and successfully delivers electrical stimuli to the second somatosensory area when triggered by neural activity from the rostral forelimb area with a user-adjustable spike-stimulus time delay. The spike-triggered ICMS is further shown to modulate the neuronal firing rate, indicating that it is physiologically effective.

Controlling selective stimulations below a spinal cord hemisection using brain recordings with a neural interface system approach

In this work we address the use of realtime cortical recordings for the generation of coherent, reliable and robust motor activity in spinal-lesioned animals through selective intraspinal microstimulation (ISMS). The spinal cord of adult rats was hemisectioned and groups of multielectrodes were implanted in both the central nervous system (CNS) and the spinal cord below the lesion level to establish a neural system interface (NSI). To test the reliability of this new NSI connection, highly repeatable neural responses recorded from the CNS were used as a pattern generator of an open-loop control strategy for selective ISMS of the spinal motoneurons. Our experimental procedure avoided the spontaneous non-controlled and non-repeatable neural activity that could have generated spurious ISMS and the consequent undesired muscle contractions. Combinations of complex CNS patterns generated precisely coordinated, reliable and robust motor actions.

The Neurochip-2: an autonomous head-fixed computer for recording and stimulating in freely behaving monkeys

The Neurochip-2 is a second generation, battery-powered device for neural recording and stimulating that is small enough to be carried in a chamber on a monkey's head. It has three recording channels, with user-adjustable gains, filters, and sampling rates, that can be optimized for recording single unit activity, local field potentials, electrocorticography, electromyography, arm acceleration, etc. Recorded data are stored on a removable, flash memory card. The Neurochip-2 also has three separate stimulation channels. Two "programmable-system-on-chips" (PSoCs) control the data acquisition and stimulus output. The PSoCs permit flexible real-time processing of the recorded data, such as digital filtering and time-amplitude window discrimination. The PSoCs can be programmed to deliver stimulation contingent on neural events or deliver preprogrammed stimuli. Access pins to the microcontroller are also available to connect external devices, such as accelerometers. The Neurochip-2 can record and stimulate autonomously for up to several days in freely behaving monkeys, enabling a wide range of novel neurophysiological and neuroengineering experiments.

Epigenetic mechanisms of mental retardation

Mental retardation is a common form of cognitive impairment affecting approximately 3% of the population in industrialized countries. The mental retardation syndrome incorporates a highly diverse group of mental disorders characterized by the combination of cognitive impairment and defective adaptive behavior. The genetic basis of the disease is strongly supported by identification of the genetic lesions associated with impaired cognition, learning, and social adaptation in many mental retardation syndromes. Several of the impaired genes encode epigenetic regulators of gene expression. These regulators exert their function through genome-wide posttranslational modification of histones or by mediating and/or recognizing DNA methylation. In this chapter, we review the most recent advances in the field of epigenetic mechanisms of mental retardation. In particular, we focus on animal models of the human diseases and the mechanism of transcriptional deregulation associated with changes in the cell epigenome.

Unconstrained three-dimensional reaching in rhesus monkeys

To better understand normative behavior for quantitative evaluation of motor recovery after injury, we studied arm movements by non-injured Rhesus monkeys during a food-retrieval task. While seated, monkeys reached, grasped, and retrieved food items. We recorded three-dimensional kinematics and muscle activity, and used inverse dynamics to calculate joint moments due to gravity, segmental interactions, and to the muscles and tissues of the arm. Endpoint paths showed curvature in three dimensions, suggesting that maintaining straight paths was not an important constraint. Joint moments were dominated by gravity. Generalized muscle and interaction moments were less than half of the gravitational moments. The relationships between shoulder and elbow resultant moments were linear during both reach and retrieval. Although both reach and retrieval required elbow flexor moments, an elbow extensor (triceps brachii) was active during both phases. Antagonistic muscles of both the elbow and hand were co-activated during reach and retrieval. Joint behavior could be described by lumped-parameter models analogous to torsional springs at the joints. Minor alterations to joint quasi-stiffness properties, aided by interaction moments, result in reciprocal movements that evolve under the influence of gravity. The strategies identified in monkeys to reach, grasp, and retrieve items will allow the quantification of prehension during recovery after a spinal cord injury and the effectiveness of therapeutic interventions.

Embedded neural recording with TinyOS-based wireless-enabled processor modules

To create a wireless neural recording system that can benefit from the continuous advancements being made in embedded microcontroller and communications technologies, an embedded-system-based architecture for wireless neural recording has been designed, fabricated, and tested. The system consists of commercial-off-the-shelf wireless-enabled processor modules (motes) for communicating the neural signals, and a back-end database server and client application for archiving and browsing the neural signals. A neural-signal-acquisition application has been developed to enable the mote to either acquire neural signals at a rate of 4000 12-bit samples per second, or detect and transmit spike heights and widths sampled at a rate of 16670 12-bit samples per second on a single channel. The motes acquire neural signals via a custom low-noise neural-signal amplifier with adjustable gain and high-pass corner frequency that has been designed, and fabricated in a 1.5-microm CMOS process. In addition to browsing acquired neural data, the client application enables the user to remotely toggle modes of operation (real-time or spike-only), as well as amplifier gain and high-pass corner frequency.

Circuit techniques for wireless brain interfaces

Efficient, miniaturized wireless recording is critical for both existing and emerging health-monitoring applications. One important example of this is in the brain interface community, where new technologies allow improved observation and understanding of brain functions. This, in turn, drives the need for ever smaller, lower power, and higher performance circuitry for chronic recording. This paper describes circuit and system techniques for low power wireless brain interfaces. Active and passive architectures are described and compared, and measured in-vivo data from both are presented.

Bidirectional telemetry controller for neuroprosthetic devices

We present versatile multifunctional programmable controller with bidirectional data telemetry, implemented using existing commercial microchips and standard Bluetooth protocol, which adds convenience, reliability, and ease-of-use to neuroprosthetic devices. Controller, weighing 190 g, is placed on animal's back and provides bidirectional sustained telemetry rate of 500 kb/s , allowing real-time control of stimulation parameters and viewing of acquired data. In continuously-active state, controller consumes approximately 420 mW and operates without recharge for 8 h . It features independent 16-channel current-controlled stimulation, allowing current steering; customizable stimulus current waveforms; recording of stimulus voltage waveforms and evoked neuronal responses with stimulus artifact blanking circuitry. Flexibility, scalability, cost-efficiency, and a user-friendly computer interface of this device allow use in animal testing for variety of neuroprosthetic applications. Initial testing of the controller has been done in a feline model of brainstem auditory prosthesis. In this model, the electrical stimulation is applied to the array of microelectrodes implanted in the ventral cochlear nucleus, while the evoked neuronal activity was recorded with the electrode implanted in the contralateral inferior colliculus. Stimulus voltage waveforms to monitor the access impedance of the electrodes were acquired at the rate of 312 kilosamples/s. Evoked neuronal activity in the inferior colliculus was recorded after the blanking (transient silencing) of the recording amplifier during the stimulus pulse, allowing the detection of neuronal responses within 100 mus after the end of the stimulus pulse applied in the cochlear nucleus.

Single-unit stability using chronically implanted multielectrode arrays

The use of chronic intracortical multielectrode arrays has become increasingly prevalent in neurophysiological experiments. However, it is not obvious whether neuronal signals obtained over multiple recording sessions come from the same or different neurons. Here, we develop a criterion to assess single-unit stability by measuring the similarity of 1) average spike waveforms and 2) interspike interval histograms (ISIHs). Neuronal activity was recorded from four Utah arrays implanted in primary motor and premotor cortices in three rhesus macaque monkeys during 10 recording sessions over a 15- to 17-day period. A unit was defined as stable through a given day if the stability criterion was satisfied on all recordings leading up to that day. We found that 57% of the original units were stable through 7 days, 43% were stable through 10 days, and 39% were stable through 15 days. Moreover, stable units were more likely to remain stable in subsequent recording sessions (i.e., 89% of the neurons that were stable through four sessions remained stable on the fifth). Using both waveform and ISIH data instead of just waveforms improved performance by reducing the number of false positives. We also demonstrate that this method can be used to track neurons across days, even during adaptation to a visuomotor rotation. Identifying a stable subset of neurons should allow the study of long-term learning effects across days and has practical implications for pooling of behavioral data across days and for increasing the effectiveness of brain-machine interfaces.

A 128-channel 6 mW wireless neural recording IC with spike feature extraction and UWB transmitter

This paper reports a 128-channel neural recording integrated circuit (IC) with on-the-fly spike feature extraction and wireless telemetry. The chip consists of eight 16-channel front-end recording blocks, spike detection and feature extraction digital signal processor (DSP), ultra wideband (UWB) transmitter, and on-chip bias generators. Each recording channel has amplifiers with programmable gain and bandwidth to accommodate different types of biological signals. An analog-to-digital converter (ADC) shared by 16 amplifiers through time-multiplexing results in a balanced trade-off between the power consumption and chip area. A nonlinear energy operator (NEO) based spike detector is implemented for identifying spikes, which are further processed by a digital frequency-shaping filter. The computationally efficient spike detection and feature extraction algorithms attribute to an auspicious DSP implementation on-chip. UWB telemetry is designed to wirelessly transfer raw data from 128 recording channels at a data rate of 90 Mbit/s. The chip is realized in 0.35 mum complementary metal-oxide-semiconductor (CMOS) process with an area of 8.8 x 7.2 mm(2) and consumes 6 mW by employing a sequential turn-on architecture that selectively powers off idle analog circuit blocks. The chip has been tested for electrical specifications and verified in an ex vivo biological environment.