This study explored the feasibility of detecting hidden muscle activity in surface electromyogram (EMG) baseline.
Power spectral density (PSD) analysis and multi-scale entropy (MSE) analysis were used respectively. Both analyses were applied to computer simulations of surface EMG baseline with presence (representing activity data) or absence (representing reference data) of hidden muscle activity, as well as surface electrode array EMG baseline recordings of healthy control and amyotrophic lateral sclerosis (ALS) subjects.
Although the simulated reference data and the activity data yielded no distinguishable difference in the time domain, they demonstrated a significant difference in the frequency and signal complexity domains with the PSD and MSE analyses. For a comparison using pooled data, such a difference was also observed when the PSD and MSE analyses were applied to surface electrode array EMG baseline recordings of healthy control and ALS subjects, which demonstrated no distinguishable difference in the time domain. Compared with the PSD analysis, the MSE analysis appeared to be more sensitive for detecting the difference in surface EMG baselines between the two groups.
The findings implied presence of hidden muscle activity in surface EMG baseline recordings from the ALS subjects. To promote the presented analysis as a useful diagnostic or investigatory tool, future studies are necessary to assess the pathophysiological nature or origins of the hidden muscle activity, as well as the baseline difference at the individual subject level.
Evaluate the suitability of four electrodes previously used in clinical experiments for peripheral nerve electrical block applications.
We evaluated peripheral nerve electrical block using three such clinical nerve cuff electrodes (the Huntington helix, the Case self-sizing spiral and the Flat Interface Nerve Electrode) and one clinical intramuscular electrode (the Memberg electrode) in five cats. Amplitude thresholds for block using 12 or 25 kHz voltage-controlled stimulation, onset response, and stimulation thresholds before and after block testing were determined.
Complete nerve block was achieved reliably and the onset response to blocking stimulation was similar for all electrodes. Amplitude thresholds for block were lowest for the Case Spiral electrode (4 ± 1 Vpp) and lower for the nerve cuff electrodes (7 ± 3 Vpp) than for the intramuscular electrode (26 ± 10 Vpp). A minor elevation in stimulation threshold and reduction in stimulus-evoked urethral pressure was observed during testing, but the effect was temporary and did not vary between electrodes.
Multiple clinical electrodes appear suitable for neuroprostheses using peripheral nerve electrical block. The freedom to choose electrodes based on secondary criteria such as ease of implantation or cost should ease translation of electrical nerve block to clinical practice.
Electrical Stimulation; Spinal Cord Injury; Rehabilitation; Urinary Bladder; Nerve Block
High-definition transcranial direct current stimulation (HD-tDCS) and high-density electroencephalography (HD-EEG) require accurate models of current flow for precise targeting and current source reconstruction. At a minimum, such modeling must capture the idiosyncratic anatomy of brain, cerebrospinal fluid (CSF) and skull for each individual subject. Currently, the process to build such high-resolution individualized models from structural magnetic resonance images (MRI) requires labor-intensive manual segmentation, even when leveraging available automated segmentation tools. Also, accurate placement of many high-density electrodes on individual scalp is a tedious procedure. The goal was to develop fully automated techniques to reduce the manual effort in such a modeling process.
A fully automated segmentation technique based on Statical Parametric Mapping 8 (SPM8), including an improved tissue probability map (TPM) and an automated correction routine for segmentation errors, was developed, along with an automated electrode placement tool for high-density arrays. The performance of these automated routines was evaluated against results from manual segmentation on 4 healthy subjects and 7 stroke patients. The criteria include segmentation accuracy, the difference of current flow distributions in resulting HD-tDCS models and the optimized current flow intensities on cortical targets.
The segmentation tool can segment out not just the brain but also provide accurate results for CSF, skull and other soft tissues with a field of view (FOV) extending to the neck. Compared to manual results, automated segmentation deviates by only 7% and 18% for normal and stroke subjects, respectively. The predicted electric fields in the brain deviate by 12% and 29% respectively, which is well within the variability observed for various modeling choices. Finally, optimized current flow intensities on cortical targets do not differ significantly.
Fully automated individualized modeling may now be feasible for large-sample EEG research studies and tDCS clinical trials.
Brain tissue undergoes dramatic molecular and cellular remodeling at the implant-tissue interface that evolves over a period of weeks after implantation. The biomechanical impact of such remodeling on the interface remains unknown. In this study, we aim to assess the changes in mechanical properties of the brain-electrode interface after chronic implantation of a microelectrode.
Microelectrodes were implanted in the rodent cortex at a depth of 1 mm for different durations - 1 day (n=4), 10-14 days (n=4), 4 weeks (n=4), 6 - 8 weeks (n=7). After the initial duration of implantation, the microelectrodes were moved an additional 1 mm downward at a constant speed of 10 μm/sec. Forces experienced by the microelectrode were measured during movement and after termination of movement. The biomechanical properties of the interfacial brain tissue were assessed from measured force-displacement curves using two separate models — a 2-parameter Mooney-Rivlin hyperelastic model and a viscoelastic model with a 2nd order prony series.
Estimated shear moduli using a 2nd order viscoelastic model increased from 0.5 - 2.6 kPa (day 1 of implantation) to 25.7 - 59.3 kPa (4 weeks of implantation) and subsequently decreased to 0.8 - 7.9 kPa after 6-8 weeks of implantation in 6 of 7 animals. Estimated elastic moduli increased from 4.1-7.8 kPa on the day of implantation to 24 - 44.9 kPa after 4 weeks. The elastic moduli was estimated to be 6.8-33.3 kPa in 6 of 7 animals after 6-8 weeks of implantation. The above estimates suggest that the brain tissue surrounding the microelectrode evolves from a stiff matrix with maximal shear and elastic moduli after 4 weeks of implantation into a composite of two different layers with different mechanical properties – a stiff compact inner layer surrounded by softer brain tissue that is biomechanically similar to brain tissue during the first week of implantation. Tissue micromotion induced stresses on the microelectrode constituted 12-55% of the steady-state stresses on the microelectrode on the day of implantation (n=4), 2-21% of the steady-state stresses after 4 weeks of implantation (n=4), and 4 - 10% of the steady-state stresses after 6-8 weeks of implantation (n=7).
Understanding the biomechanical behavior at the brain-microelectrode interface is necessary for long-term success of implantable neuroprosthetics and microelectrode arrays. Such quantitative physical characterization of the dynamic changes in the electrode-tissue interface will (a) drive design and development of more mechanically optimal, chronic brain implants and (b) will lead to new insights into key cellular and molecular events such as neuronal adhesion, migration and function in the immediate vicinity of the brain implant.
Memory accuracy is a major problem in human disease and is the primary factor that defines Alzheimer’s’, aging and dementia resulting from impaired hippocampal function in medial temporal lobe. Development of a hippocampal memory neuroprosthesis that facilitates normal memory encoding in nonhuman primates (NHPs) could provide the basis for improving memory in human disease states.
NHPs trained to perform a short-term delayed match to sample (DMS) memory task were examined with multi-neuron recordings from synaptically connected hippocampal cell fields, CA1 and CA3. Recordings were analyzed utilizing a previously developed nonlinear multi-input multi-output (MIMO) neuroprosthetic model, capable of extracting CA3-to-CA1 spatiotemporal firing patterns during DMS performance.
The MIMO model verified that specific CA3-to-CA1 firing patterns were critical for successful encoding of Sample phase information on more difficult DMS trials. This was validated by delivery of successful MIMO-derived encoding patterns via electrical stimulation to the same CA1 recording locations during the Sample phase which facilitated task performance in the subsequent delayed Match phase on difficult trials that required more precise encoding of Sample information.
These findings provide the first successful application of a neuroprosthesis designed to enhance and/or repair memory encoding in primate brain.
Hippocampal neuron; spike train; memory encoding; nonlinear model; patterned electrical stimulation; prosthesis; prosthetic; memory facilitation; memory retention; nonhuman primate
Analyzing and interpreting the activity of a heterogeneous population of neurons can be challenging, especially as the number of neurons, experimental trials, and experimental conditions increases. One approach is to extract a set of latent variables that succinctly captures the prominent co-fluctuation patterns across the neural population. A key problem is that the number of latent variables needed to adequately describe the population activity is often greater than three, thereby preventing direct visualization of the latent space. By visualizing a small number of 2-d projections of the latent space or each latent variable individually, it is easy to miss salient features of the population activity.
To address this limitation, we developed a Matlab graphical user interface (called DataHigh) that allows the user to quickly and smoothly navigate through a continuum of different 2-d projections of the latent space. We also implemented a suite of additional visualization tools (including playing out population activity timecourses as a movie and displaying summary statistics, such as covariance ellipses and average timecourses) and an optional tool for performing dimensionality reduction.
To demonstrate the utility and versatility of DataHigh, we used it to analyze single-trial spike count and single-trial timecourse population activity recorded using a multi-electrode array, as well as trial-averaged population activity recorded using single electrodes.
DataHigh was developed to fulfill a need for visualization in exploratory neural data analysis, which can provide intuition that is critical for building scientific hypotheses and models of population activity.
Nonlinear system identification approaches were used to develop a dynamical model of the network level response to patterns of microstimulation in-vivo.
The thalamocortical circuit of the rodent vibrissa pathway was the model system, with voltage sensitive dye imaging capturing the cortical response to patterns of stimulation delivered from a single electrode in the ventral posteromedial thalamus. The results of simple paired stimulus experiments formed the basis for the development of a phenomenological model explicitly containing nonlinear elements observed experimentally. The phenomenological model was fit using datasets obtained with impulse train inputs, Poisson-distributed in time and uniformly varying in amplitude.
The phenomenological model explained 58% of the variance in the cortical response to out of sample patterns of thalamic microstimulation. Furthermore, while fit on trial averaged data, the phenomenological model reproduced single trial response properties when simulated with noise added into the system during stimulus presentation. The simulations indicate that the single trial response properties were dependent on the relative sensitivity of the static nonlinearities in the two stages of the model, and ultimately suggest that electrical stimulation activates local circuitry through linear recruitment, but that this activity propagates in a highly nonlinear fashion to downstream targets.
The development of nonlinear dynamical models of neural circuitry will guide information delivery for sensory prosthesis applications, and more generally reveal properties of population coding within neural circuits.
To increase the symbol rate of the electroencephalography (EEG) based brain computer interface (BCI) typing systems by utilizing the context information.
Event related potentials (ERP) corresponding to a stimulus in EEG can be used to detect the intended target of a person for BCI. This paradigm is widely utilized to build letter-by-letter BCI typing systems. Nevertheless currently available BCI-typing systems still requires improvement due to low typing speeds. This is mainly due to the reliance on multiple repetitions before making a decision to achieve a higher typing accuracy. Another possible approach to increase the speed of typing while not significantly reducing the accuracy of typing is to use additional context information. In this paper, we study the effect of using a language model as additional evidence for intent detection. Bayesian fusion of an n-gram symbol model with the EEG features is proposed, and specifically regularized discriminant analysis ERP discriminant is used to obtain EEG-based features. The target detection accuracies are rigorously evaluated for varying language model orders, as well as the number of ERP-inducing repetitions.
The results demonstrate that the language models contribute significantly to letter classification accuracy. For instance, we find that a single-trial ERP detection supported by a 4-gram language model may achieve the same performance as using 3-trial ERP classification for the non-initial letters of words.
Overall, fusion of evidence from EEG and language models yields a significant opportunity to increase the symbol rate of a BCI typing system.
Cortical electrical stimulation (CES) has been used extensively in experimental neuroscience to modulate neuronal or behavioral activity, which has led this technique to be considered in neurorehabilitation. Because the cortex and the surrounding anatomy have irregular geometries as well as inhomogeneous and anisotropic electrical properties, the mechanisms by which CES has therapeutic effects is poorly understood. Therapeutic effects of CES can be improved by optimizing the stimulation parameters based on the effects of various stimulation parameters on target brain regions.
In this study we have compared the effects of CES pulse polarity, frequency, and amplitude on unit activity recorded from rat primary motor cortex with the effects on the corresponding local field potentials (LFP), and electrocorticograms (ECoG). CES was applied at the surface of the cortex and the unit activity and LFPs were recorded using a penetrating electrode array, which was implanted below the stimulation site. ECoGs were recorded from the vicinity of the stimulation site.
Time-frequency analysis of LFPs following CES showed correlation of gamma frequencies with unit activity response in all layers. More importantly, high gamma power of ECoG signals only correlated with the unit activity in lower layers (V-VI) following CES. Time-frequency correlations, which were found between LFPs, ECoGs and unit activity, were frequency- and amplitude-dependent.
The signature of the neural activity observed in LFP and ECoG signals provides a better understanding of the effects of stimulation on network activity, representative of large numbers of neurons responding to stimulation. These results demonstrate that the neurorehabilitation and neuroprosthetic applications of CES targeting layered cortex can be further improved by using field potential recordings as surrogates to unit activity aimed at optimizing stimulation efficacy. Likewise, the signatures of unit activity observed as changes in high-gamma power in ECoGs suggest that future cortical stimulation studies could rely on less invasive feedback schemes that incorporate surface stimulation with ECoG reporting of stimulation efficacy.
Cortical electrical stimulation; unit activity; local field potentials; electrocorticograms; primary motor cortex
Bionic devices electrically activate neural populations to partially restore lost function. Of fundamental importance is the functional integrity of the targeted neurons. However, in many conditions the ongoing pathology can lead to continued neural degeneration and death that may compromise the effectiveness of the device and limit future strategies to improve performance. The use of drugs that can prevent nerve cell degeneration and promote their regeneration may improve clinical outcomes. In this paper we focus on strategies of delivering neuroprotective drugs to the auditory system in a way that is safe and clinically relevant for use in combination with a cochlear implant. The aim of this approach is to prevent neural degeneration and promote nerve regrowth in order to improve outcomes for cochlear implant recipients using techniques that can be translated to the clinic.
drug delivery; cell-based therapy bionics; neural protection; neurotrophins; electrical stimulation
Brain machine interfaces (BMIs) that decode control signals from motor cortex have developed tremendously in the past decade, but virtually all rely exclusively on vision to provide feedback. There is now increasing interest in developing an afferent interface to replace natural somatosensation, much as the cochlear implant has done for the sense of hearing. Preliminary experiments toward a somatosensory neuroprosthesis have mostly addressed the sense of touch, but proprioception, the sense of limb position and movement, is also critical for the control of movement. However, proprioceptive areas of cortex lack the precise somatotopy of tactile areas. We showed previously that there is only a weak tendency for neighboring neurons in area 2 to signal similar directions of hand movement. Consequently, stimulation with the relatively large currents used in many studies is likely to activate a rather heterogeneous set of neurons. Here, we have compared the effect of single-electrode stimulation at sub-threshold levels to the effect of stimulating as many as seven electrodes in combination. We found a mean enhancement in the sensitivity to the stimulus (d′) of 0.17 for pairs compared to individual electrodes (an increase of roughly 30%), and an increase of 2.5 for groups of seven electrodes (260%). We propose that a proprioceptive interface made up of several hundred electrodes may yield safer, more effective sensation than a BMI using fewer electrodes and larger currents.
Monkey; Area 2; intracortical microstimulation
Brain machine interfaces (BMIs) have the potential to restore movement to people with paralysis. However, a clinically-viable BMI must enable consistently accurate control over time spans ranging from years to decades, which has not yet been demonstrated. Most BMIs that use single-unit spikes as inputs will experience degraded performance over time without frequent decoder re-training. Two other signals, local field potentials (LFPs) and multi-unit spikes (MSPs), may offer greater reliability over long periods and better performance stability than single-unit spikes. Here, we demonstrate that LFPs can be used in a biomimetic BMI to control a computer cursor.
We implanted two rhesus macaques with intracortical microelectrodes in primary motor cortex. We recorded LFP and MSP signals from the monkeys while they performed a continuous reaching task, moving a cursor to randomly-placed targets on a computer screen. We then used the LFP and MSP signals to construct biomimetic decoders for control of the cursor.
Both monkeys achieved high-performance, continuous control that remained stable or improved over nearly 12 months using an LFP decoder that was not retrained or adapted. In parallel, the monkeys used MSPs to control a BMI without retraining or adaptation and had similar or better performance, and that predominantly remained stable over more than six months. In contrast to their stable online control, both LFP and MSP signals showed substantial variability when used offline to predict hand movements.
Our results suggest that the monkeys were able to stabilize the relationship between neural activity and cursor movement during online BMI control, despite variability in the relationship between neural activity and hand movements.
Optogenetics promises exciting neuroscience research by offering optical stimulation of neurons with unprecedented temporal resolution, cell-type specificity and the ability to excite as well as to silence neurons. This work provides the technical solution to deliver light to local neurons and record neural potentials, facilitating local circuit analysis and bridging the gap between optogenetics and neurophysiology research.
We have designed and obtained the first in vivo validation of a neural probe with monolithically integrated electrodes and waveguide. High spatial precision enables optical excitation of targeted neurons with minimal power and recording of single-units in dense cortical and subcortical regions.
The total coupling and transmission loss through the dielectric waveguide at 473 nm was 10.5 ± 1.9 dB, corresponding to an average output intensity of 9400 mW mm−2 when coupled to a 7 mW optical fiber. Spontaneous field potentials and spiking activities of multiple Channelrhodopsin-2 expressing neurons were recorded in the hippocampus CA1 region of an anesthetized rat. Blue light stimulation at intensity of 51 mW mm−2 induced robust spiking activities in the physiologically identified local populations.
This minimally invasive, complete monolithic integration provides unmatched spatial precision and scalability for future optogenetics studies at deep brain regions with high neuronal density.
Clinical deep brain stimulation (DBS) systems can be programmed with thousands of different stimulation parameter combinations (e.g. electrode contact(s), voltage, pulse width, frequency). Our goal was to develop novel computational tools to characterize the effects of stimulation parameter adjustment for DBS.
The volume of tissue activated (VTA) represents a metric used to estimate the spatial extent of DBS for a given parameter setting. Traditional methods for calculating the VTA rely on activation function (AF)-based approaches and tend to overestimate the neural response when stimulation is applied through multiple electrode contacts. Therefore, we created a new method for VTA calculation that relied on artificial neural networks (ANNs).
The ANN-based predictor provides more accurate descriptions of the spatial spread of activation compared to AF-based approaches for monopolar stimulation. In addition, the ANN was able to accurately estimate the VTA in response to multi-contact electrode configurations.
The ANN-based approach may represent a useful method for fast computation of the VTA in situations with limited computational resources, such as a clinical DBS programming application on a tablet computer.
Computational modeling; axon; activation
The goal of this study was to decode sensory information from the dorsal root ganglia (DRG) in real time, and to use this information to adapt the control of unilateral stepping with a state-based control algorithm consisting of both feed-forward and feedback components.
In five anesthetized cats, hind limb stepping on a walkway or treadmill was produced by patterned electrical stimulation of the spinal cord through implanted microwire arrays, while neuronal activity was recorded from the dorsal root ganglia. Different parameters, including distance and tilt of the vector between hip and limb endpoint, integrated gyroscope and ground reaction force were modeled from recorded neural firing rates. These models were then used for closed-loop feedback.
Overall, firing-rate based predictions of kinematic sensors (limb endpoint, integrated gyroscope) were the most accurate with variance accounted for >60% on average. Force prediction had the lowest prediction accuracy (48±13%) but produced the greatest percentage of successful rule activations (96.3%) for stepping under closed-loop feedback control. The prediction of all sensor modalities degraded over time, with the exception of tilt.
Sensory feedback from moving limbs would be a desirable component of any neuroprosthetic device designed to restore walking in people after a spinal cord injury. This study provides a proof-of-principle that real-time feedback from the DRG is possible and could form part of a fully implantable neuroprosthetic device with further development.
Electrical stimulation has been shown effective in restoring basic lower extremity motor function in individuals with paralysis. We tested the hypothesis that a Flat Interface Nerve Electrode (FINE) placed around the human tibial or common peroneal nerve above the knee can selectively activate each of the most important muscles these nerves innervate for use in a neuroprosthesis to control ankle motion.
During intraoperative trials involving three subjects, an 8-contact FINE was placed around the tibial and/or common peroneal nerve, proximal to the popliteal fossa. The FINE’s ability to selectively recruit muscles innervated by these nerves was assessed. Data were used to estimate the potential to restore active plantarflexion or dorsiflexion while balancing inversion and eversion using a biomechanical simulation.
Results, Significance, and Potential Impact
With minimal spillover to non-targets, at least three of the four targets in the tibial nerve, including two of the three muscles constituting the triceps surae were independently and selectively recruited in all subjects. As acceptable levels of spillover increased, recruitment of the target muscles increased. Selective activation of muscles innervated by the peroneal nerve was more challenging. Estimated joint moments suggests that plantarflexion sufficient for propulsion during stance phase of gait and dorsiflexion sufficient to prevent foot drop during swing can be achieved, accompanied by a small but tolerable inversion or eversion moment.
Functional Electrical Stimulation (FES); Flat Interface Nerve Electrode (FINE); Human; Tibial; Fibular; Peroneal; Plantarflexion; Dorsiflexion
We present a holographic near-the-eye display system enabling optical approaches for sight restoration to the blind, such as photovoltaic retinal prosthesis, optogenetic and other photoactivation techniques. We compare it with conventional LCD or DLP-based displays in terms of image quality, field of view, optical efficiency and safety.
We detail the optical configuration of the holographic display system and its characterization using a phase-only spatial light modulator.
We describe approaches to controlling the zero diffraction order and speckle related issues in holographic display systems and assess the image quality of such systems. We show that holographic techniques offer significant advantages in terms of peak irradiance and power efficiency, and enable designs that are inherently safer than LCD or DLP-based systems. We demonstrate the performance of our holographic display system in the assessment of cortical response to alternating gratings projected onto the retinas of rats.
We address the issues associated with the design of high brightness, near-the-eye display systems and propose solutions to the efficiency and safety challenges with an optical design which could be miniaturized and mounted onto goggles.
Support Vector Machines (SVM) have developed into a gold standard for accurate classification in Brain-Computer-Interfaces (BCI). The choice of the most appropriate classifier for a particular application depends on several characteristics in addition to decoding accuracy. Here we investigate the implementation of Hidden Markov Models (HMM)for online BCIs and discuss strategies to improve their performance.
We compare the SVM, serving as a reference, and HMMs for classifying discrete finger movements obtained from the Electrocorticograms of four subjects doing a finger tapping experiment. The classifier decisions are based on a subset of low-frequency time domain and high gamma oscillation features.
We show that decoding optimization between the two approaches is due to the way features are extracted and selected and less dependent on the classifier. An additional gain in HMM performance of up to 6% was obtained by introducing model constraints. Comparable accuracies of up to 90% were achieved with both SVM and HMM with the high gamma cortical response providing the most important decoding information for both techniques.
We discuss technical HMM characteristics and adaptations in the context of the presented data as well as for general BCI applications. Our findings suggest that HMMs and their characteristics are promising for efficient online brain-computer interfaces.
Hidden Markov Models; ECoG; finger movements; support vector machine; Bakis; event-related potentials; spectral perturbation
We recently introduced a series of stimuli-responsive, mechanically-adaptive polymer nanocomposites. Here, we report the first application of these bio-inspired materials as substrates for intracortical microelectrodes. Our hypothesis is that the ideal electrode should be initially stiff to facilitate minimal trauma during insertion into the cortex, yet becomes mechanically compliant to match the stiffness of the brain tissue and minimize forces exerted on the tissue, attenuating inflammation. Microprobes created from mechanically reinforced nanocomposites demonstrated a significant advantage compared to model microprobes composed of neat polymer only. The nanocomposite microprobes exhibit a higher storage modulus (E’ = ~5 GPa) than the neat polymer microprobes (E’ = ~2 GPa) and could sustain higher loads (~17 mN), facilitating penetration through the pia mater and insertion into the cerebral cortex of a rat. In contrast, the neat polymer microprobes mechanically failed under lower loads (~7 mN) before they were capable of inserting into cortical tissue. Further, we demonstrated the material’s ability to morph while in the rat cortex to more closely match the mechanical properties of the cortical tissue. Nanocomposite microprobes that were implanted into the rat cortex for up to 8 weeks demonstrated increased cell density at the microelectrode-tissue interface and a lack of tissue necrosis or excessive gliosis. This body of work introduces our nanocomposite-based microprobes as adaptive substrates for intracortical microelectrodes and potentially other biomedical applications.
Brain, Neural Prosthesis; Young’s Modulus; Insertion Force; Mechanical properties; Nanocomposite
At the balanced intersection of human and machine adaptation is found the optimally functioning brain-computer interface (BCI). In this study, we report a novel experiment of BCI controlling a robotic quadcopter in three-dimensional physical space using noninvasive scalp EEG in human subjects. We then quantify the performance of this system using metrics suitable for asynchronous BCI. Lastly, we examine the impact that operation of a real world device has on subjects’ control with comparison to a two-dimensional virtual cursor task.
Five human subjects were trained to modulate their sensorimotor rhythms to control an AR Drone navigating a three-dimensional physical space. Visual feedback was provided via a forward facing camera on the hull of the drone. Individual subjects were able to accurately acquire up to 90.5% of all valid targets presented while travelling at an average straight-line speed of 0.69 m/s.
Freely exploring and interacting with the world around us is a crucial element of autonomy that is lost in the context of neurodegenerative disease. Brain-computer interfaces are systems that aim to restore or enhance a user’s ability to interact with the environment via a computer and through the use of only thought. We demonstrate for the first time the ability to control a flying robot in the three-dimensional physical space using noninvasive scalp recorded EEG in humans. Our work indicates the potential of noninvasive EEG based BCI systems to accomplish complex control in three-dimensional physical space. The present study may serve as a framework for the investigation of multidimensional non-invasive brain-computer interface control in a physical environment using telepresence robotics.
Brain-Computer Interface; BCI; EEG; 3D control; motor imagery; telepresence robotics
Intraspinal microstimulation (ISMS) is a promising method for activating the spinal cord distal to an injury. The objectives of this study were to examine the ability of chronically implanted stimulating wires within the cervical spinal cord to (1) directly produce forelimb movements, and (2) assess whether ISMS stimulation improved subsequent volitional control of paretic extremities following injury.
We developed a technique for implanting intraspinal stimulating electrodes within the cervical spinal cord segments C6-T1 of Long-Evans rats. Beginning 4 weeks after a severe cervical contusion injury at C4–C5, animals in the treatment condition received therapeutic ISMS 7 hours/day, 5 days/week for the following 12 weeks.
Over 12 weeks of therapeutic ISMS, stimulus-evoked forelimb movements were relatively stable. We also explored whether therapeutic ISMS promotes recovery of forelimb reaching movements. Animals receiving daily therapeutic ISMS performed significantly better than unstimulated animals during behavioral tests conducted without stimulation. Quantitative video analysis of forelimb movements showed that stimulated animals performed better in the movements reinforced by stimulation, including extending the elbow to advance the forelimb and opening the digits. While threshold current to elicit forelimb movement gradually increased over time, no differences were observed between chronically stimulated and unstimulated electrodes suggesting that no additional tissue damage was produced by the electrical stimulation.
The results indicate that therapeutic intraspinal stimulation delivered via chronic microwire implants within the cervical spinal cord confers benefits extending beyond the period of stimulation, suggesting future strategies for neural devices to promote sustained recovery after injury.
spinal cord injury; ISMS; regenerative stimulation; neuroprosthesis