Showing papers in "Journal of Neural Engineering in 2017"

A novel deep learning approach for classification of EEG motor imagery signals

Abstract: Objective. Signal classification is an important issue in brain computer interface (BCI) systems. Deep learning approaches have been used successfully in many recent studies to learn features and classify different types of data. However, the number of studies that employ these approaches on BCI applications is very limited. In this study we aim to use deep learning methods to improve classification performance of EEG motor imagery signals. Approach. In this study we investigate convolutional neural networks (CNN) and stacked autoencoders (SAE) to classify EEG Motor Imagery signals. A new form of input is introduced to combine time, frequency and location information extracted from EEG signal and it is used in CNN having one 1D convolutional and one max-pooling layers. We also proposed a new deep network by combining CNN and SAE. In this network, the features that are extracted in CNN are classified through the deep network SAE. Main results. The classification performance obtained by the proposed method on BCI competition IV dataset 2b in terms of kappa value is 0.547. Our approach yields 9% improvement over the winner algorithm of the competition. Significance. Our results show that deep learning methods provide better classification performance compared to other state of art approaches. These methods can be applied successfully to BCI systems where the amount of data is large due to daily recording.

Open Ephys: an open-source, plugin-based platform for multichannel electrophysiology.

Abstract: Objective. Closed-loop experiments, in which causal interventions are conditioned on the state of the system under investigation, have become increasingly common in neuroscience. Such experiments can have a high degree of explanatory power, but they require a precise implementation that can be difficult to replicate across laboratories. We sought to overcome this limitation by building open-source software that makes it easier to develop and share algorithms for closed-loop control. Approach. We created the Open Ephys GUI, an open-source platform for multichannel electrophysiology experiments. In addition to the standard 'open-loop' visualization and recording functionality, the GUI also includes modules for delivering feedback in response to events detected in the incoming data stream. Importantly, these modules can be built and shared as plugins, which makes it possible for users to extend the functionality of the GUI through a simple API, without having to understand the inner workings of the entire application. Main results. In combination with low-cost, open-source hardware for amplifying and digitizing neural signals, the GUI has been used for closed-loop experiments that perturb the hippocampal theta rhythm in a phase-specific manner. Significance. The Open Ephys GUI is the first widely used application for multichannel electrophysiology that leverages a plugin-based workflow. We hope that it will lower the barrier to entry for electrophysiologists who wish to incorporate real-time feedback into their research.

A multimodal approach to estimating vigilance using EEG and forehead EOG.

Abstract: Objective Covert aspects of ongoing user mental states provide key context information for user-aware human computer interactions. In this paper, we focus on the problem of estimating the vigilance of users using EEG and EOG signals. Approach The PERCLOS index as vigilance annotation is obtained from eye tracking glasses. To improve the feasibility and wearability of vigilance estimation devices for real-world applications, we adopt a novel electrode placement for forehead EOG and extract various eye movement features, which contain the principal information of traditional EOG. We explore the effects of EEG from different brain areas and combine EEG and forehead EOG to leverage their complementary characteristics for vigilance estimation. Considering that the vigilance of users is a dynamic changing process because the intrinsic mental states of users involve temporal evolution, we introduce continuous conditional neural field and continuous conditional random field models to capture dynamic temporal dependency. Main results We propose a multimodal approach to estimating vigilance by combining EEG and forehead EOG and incorporating the temporal dependency of vigilance into model training. The experimental results demonstrate that modality fusion can improve the performance compared with a single modality, EOG and EEG contain complementary information for vigilance estimation, and the temporal dependency-based models can enhance the performance of vigilance estimation. From the experimental results, we observe that theta and alpha frequency activities are increased, while gamma frequency activities are decreased in drowsy states in contrast to awake states. Significance The forehead setup allows for the simultaneous collection of EEG and EOG and achieves comparative performance using only four shared electrodes in comparison with the temporal and posterior sites.

Deep learning-based artificial vision for grasp classification in myoelectric hands.

Abstract: OBJECTIVE Computer vision-based assistive technology solutions can revolutionise the quality of care for people with sensorimotor disorders. The goal of this work was to enable trans-radial amputees to use a simple, yet efficient, computer vision system to grasp and move common household objects with a two-channel myoelectric prosthetic hand. APPROACH We developed a deep learning-based artificial vision system to augment the grasp functionality of a commercial prosthesis. Our main conceptual novelty is that we classify objects with regards to the grasp pattern without explicitly identifying them or measuring their dimensions. A convolutional neural network (CNN) structure was trained with images of over 500 graspable objects. For each object, 72 images, at [Formula: see text] intervals, were available. Objects were categorised into four grasp classes, namely: pinch, tripod, palmar wrist neutral and palmar wrist pronated. The CNN setting was first tuned and tested offline and then in realtime with objects or object views that were not included in the training set. MAIN RESULTS The classification accuracy in the offline tests reached [Formula: see text] for the seen and [Formula: see text] for the novel objects; reflecting the generalisability of grasp classification. We then implemented the proposed framework in realtime on a standard laptop computer and achieved an overall score of [Formula: see text] in classifying a set of novel as well as seen but randomly-rotated objects. Finally, the system was tested with two trans-radial amputee volunteers controlling an i-limb UltraTM prosthetic hand and a motion controlTM prosthetic wrist; augmented with a webcam. After training, subjects successfully picked up and moved the target objects with an overall success of up to [Formula: see text]. In addition, we show that with training, subjects' performance improved in terms of time required to accomplish a block of 24 trials despite a decreasing level of visual feedback. SIGNIFICANCE The proposed design constitutes a substantial conceptual improvement for the control of multi-functional prosthetic hands. We show for the first time that deep-learning based computer vision systems can enhance the grip functionality of myoelectric hands considerably.

Single-channel in-ear-EEG detects the focus of auditory attention to concurrent tone streams and mixed speech.

Abstract: Objective. Conventional, multi-channel scalp electroencephalography (EEG) allows the identification of the attended speaker in concurrent-listening ('cocktail party') scenarios. This implies that EEG might provide valuable information to complement hearing aids with some form of EEG and to install a level of neuro-feedback. Approach. To investigate whether a listener's attentional focus can be detected from single-channel hearing-aid-compatible EEG configurations, we recorded EEG from three electrodes inside the ear canal ('in-Ear-EEG') and additionally from 64 electrodes on the scalp. In two different, concurrent listening tasks, participants (n = 7) were fitted with individualized in-Ear-EEG pieces and were either asked to attend to one of two dichotically-presented, concurrent tone streams or to one of two diotically-presented, concurrent audiobooks. A forward encoding model was trained to predict the EEG response at single EEG channels. Main results. Each individual participants' attentional focus could be detected from single-channel EEG response recorded from short-distance configurations consisting only of a single in-Ear-EEG electrode and an adjacent scalp-EEG electrode. The differences in neural responses to attended and ignored stimuli were consistent in morphology (i.e. polarity and latency of components) across subjects. Significance. In sum, our findings show that the EEG response from a single-channel, hearing-aid-compatible configuration provides valuable information to identify a listener's focus of attention.

An open source, wireless capable miniature microscope system

Abstract: Objective Fluorescence imaging through head-mounted microscopes in freely behaving animals is becoming a standard method to study neural circuit function Flexible, open-source designs are needed to spur evolution of the method Approach We describe a miniature microscope for single-photon fluorescence imaging in freely behaving animals The device is made from 3D printed parts and off-the-shelf components These microscopes weigh less than 18 g, can be configured to image a variety of fluorophores, and can be used wirelessly or in conjunction with active commutators Microscope control software, based in Swift for macOS, provides low-latency image processing capabilities for closed-loop, or BMI, experiments Main results Miniature microscopes were deployed in the songbird premotor region HVC (used as a proper name), in singing zebra finches Individual neurons yield temporally precise patterns of calcium activity that are consistent over repeated renditions of song Several cells were tracked over timescales of weeks and months, providing an opportunity to study learning related changes in HVC Significance 3D printed miniature microscopes, composed completely of consumer grade components, are a cost-effective, modular option for head-mounting imaging These easily constructed and customizable tools provide access to cell-type specific neural ensembles over timescales of weeks

Neural decoding of attentional selection in multi-speaker environments without access to clean sources

Abstract: Objective People who suffer from hearing impairments can find it difficult to follow a conversation in a multi-speaker environment. Current hearing aids can suppress background noise; however, there is little that can be done to help a user attend to a single conversation amongst many without knowing which speaker the user is attending to. Cognitively controlled hearing aids that use auditory attention decoding (AAD) methods are the next step in offering help. Translating the successes in AAD research to real-world applications poses a number of challenges, including the lack of access to the clean sound sources in the environment with which to compare with the neural signals. We propose a novel framework that combines single-channel speech separation algorithms with AAD. Approach We present an end-to-end system that (1) receives a single audio channel containing a mixture of speakers that is heard by a listener along with the listener's neural signals, (2) automatically separates the individual speakers in the mixture, (3) determines the attended speaker, and (4) amplifies the attended speaker's voice to assist the listener. Main results Using invasive electrophysiology recordings, we identified the regions of the auditory cortex that contribute to AAD. Given appropriate electrode locations, our system is able to decode the attention of subjects and amplify the attended speaker using only the mixed audio. Our quality assessment of the modified audio demonstrates a significant improvement in both subjective and objective speech quality measures. Significance Our novel framework for AAD bridges the gap between the most recent advancements in speech processing technologies and speech prosthesis research and moves us closer to the development of cognitively controlled hearable devices for the hearing impaired.

Automated EEG artifact elimination by applying machine learning algorithms to ICA-based features.

Abstract: Objective. Biological and non-biological artifacts cause severe problems when dealing with electroencephalogram (EEG) recordings. Independent component analysis (ICA) is a widely used method for eliminating various artifacts from recordings. However, evaluating and classifying the calculated independent components (IC) as artifact or EEG is not fully automated at present. Approach. In this study, we propose a new approach for automated artifact elimination, which applies machine learning algorithms to ICA-based features. Main results. We compared the performance of our classifiers with the visual classification results given by experts. The best result with an accuracy rate of 95% was achieved using features obtained by range filtering of the topoplots and IC power spectra combined with an artificial neural network. Significance. Compared with the existing automated solutions, our proposed method is not limited to specific types of artifacts, electrode configurations, or number of EEG channels. The main advantages of the proposed method is that it provides an automatic, reliable, real-time capable, and practical tool, which avoids the need for the time-consuming manual selection of ICs during artifact removal.

Support vector machines to detect physiological patterns for EEG and EMG-based human-computer interaction: a review

Abstract: Support vector machines (SVMs) are widely used classifiers for detecting physiological patterns in human-computer interaction (HCI). Their success is due to their versatility, robustness and large availability of free dedicated toolboxes. Frequently in the literature, insufficient details about the SVM implementation and/or parameters selection are reported, making it impossible to reproduce study analysis and results. In order to perform an optimized classification and report a proper description of the results, it is necessary to have a comprehensive critical overview of the applications of SVM. The aim of this paper is to provide a review of the usage of SVM in the determination of brain and muscle patterns for HCI, by focusing on electroencephalography (EEG) and electromyography (EMG) techniques. In particular, an overview of the basic principles of SVM theory is outlined, together with a description of several relevant literature implementations. Furthermore, details concerning reviewed papers are listed in tables and statistics of SVM use in the literature are presented. Suitability of SVM for HCI is discussed and critical comparisons with other classifiers are reported.

Histological evaluation of flexible neural implants; flexibility limit for reducing the tissue response?

Abstract: Objective. Flexible neural probes are hypothesized to reduce the chronic foreign body response (FBR) mainly by reducing the strain-stress caused by an interplay between the tethered probe and the brain's micromotion. However, a large discrepancy of Young's modulus still exists (3-6 orders of magnitude) between the flexible probes and the brain tissue. This raises the question of whether we need to bridge this gap; would increasing the probe flexibility proportionally reduce the FBR? Approach. Using novel off-stoichiometry thiol-enes-epoxy (OSTE+) polymer probes developed in our previous work, we quantitatively evaluated the FBR to four types of probes with different softness: silicon (∼150 GPa), polyimide (1.5 GPa), OSTE+Hard (300 MPa), and OSTE+Soft (6 MPa). Main results. We observed a significant reduction in the fluorescence intensity of biomarkers for activated microglia/macrophages and blood-brain barrier (BBB) leakiness around the three soft polymer probes compared to the silicon probe, both at 4 weeks and 8 weeks post-implantation. However, we did not observe any consistent differences in the biomarkers among the polymer probes. Significance. The results suggest that the mechanical compliance of neural probes can mediate the degree of FBR, but its impact diminishes after a hypothetical threshold level. This infers that resolving the mechanical mismatch alone has a limited effect on improving the lifetime of neural implants. (Less)

Numerical evaluation of the skull for human neuromodulation with transcranial focused ultrasound.

Abstract: Objective. Transcranial focused ultrasound is an emerging field for human non-invasive neuromodulation, but its dosing in humans is difficult to know due to the skull. The objective of the present study was to establish modeling methods based on medical images to assess skull differences between individuals on the wave propagation of ultrasound. Approach. Computational models of transcranial focused ultrasound were constructed using CT and MR scans to solve for intracranial pressure. We explored the effect of including the skull base in models, different transducer placements on the head, and differences between 250 kHz or 500 kHz acoustic frequency for both female and male models. We further tested these features using linear, nonlinear, and elastic simulations. To better understand inter-subject skull thickness and composition effects we evaluated the intracranial pressure maps between twelve individuals at two different skull sites. Main results. Nonlinear acoustic simulations resulted in virtually identical intracranial pressure maps with linear acoustic simulations. Elastic simulations showed a difference in max pressures and full width half maximum volumes of 15% at most. Ultrasound at an acoustic frequency of 250 kHz resulted in the creation of more prominent intracranial standing waves compared to 500 kHz. Finally, across twelve model human skulls, a significant linear relationship to characterize intracranial pressure maps was not found. Significance. Despite its appeal, an inherent problem with the use of a noninvasive transcranial ultrasound method is the difficulty of knowing intracranial effects because of the skull. Here we develop detailed computational models derived from medical images of individuals to simulate the propagation of neuromodulatory ultrasound across the skull and solve for intracranial pressure maps. These methods allow for a much better understanding of the intracranial effects of ultrasound for an individual in order to ensure proper targeting and more tightly control dosing.

Learning to see again: biological constraints on cortical plasticity and the implications for sight restoration technologies

Abstract: The 'bionic eye'-so long a dream of the future-is finally becoming a reality with retinal prostheses available to patients in both the US and Europe. However, clinical experience with these implants has made it apparent that the visual information provided by these devices differs substantially from normal sight. Consequently, the ability of patients to learn to make use of this abnormal retinal input plays a critical role in whether or not some functional vision is successfully regained. The goal of the present review is to summarize the vast basic science literature on developmental and adult cortical plasticity with an emphasis on how this literature might relate to the field of prosthetic vision. We begin with describing the distortion and information loss likely to be experienced by visual prosthesis users. We then define cortical plasticity and perceptual learning, and describe what is known, and what is unknown, about visual plasticity across the hierarchy of brain regions involved in visual processing, and across different stages of life. We close by discussing what is known about brain plasticity in sight restoration patients and discuss biological mechanisms that might eventually be harnessed to improve visual learning in these patients.

The hybrid BCI system for movement control by combining motor imagery and moving onset visual evoked potential.

Abstract: Objective Movement control is an important application for EEG-BCI (EEG-based brain-computer interface) systems. A single-modality BCI cannot provide an efficient and natural control strategy, but a hybrid BCI system that combines two or more different tasks can effectively overcome the drawbacks encountered in single-modality BCI control. Approach In the current paper, we developed a new hybrid BCI system by combining MI (motor imagery) and mVEP (motion-onset visual evoked potential), aiming to realize the more efficient 2D movement control of a cursor. Main result The offline analysis demonstrates that the hybrid BCI system proposed in this paper could evoke the desired MI and mVEP signal features simultaneously, and both are very close to those evoked in the single-modality BCI task. Furthermore, the online 2D movement control experiment reveals that the proposed hybrid BCI system could provide more efficient and natural control commands. Significance The proposed hybrid BCI system is compensative to realize efficient 2D movement control for a practical online system, especially for those situations in which P300 stimuli are not suitable to be applied.

Continuous decoding of human grasp kinematics using epidural and subdural signals.

Abstract: OBJECTIVE Restoring or replacing function in paralyzed individuals will one day be achieved through the use of brain-machine interfaces. Regaining hand function is a major goal for paralyzed patients. Two competing prerequisites for the widespread adoption of any hand neuroprosthesis are accurate control over the fine details of movement, and minimized invasiveness. Here, we explore the interplay between these two goals by comparing our ability to decode hand movements with subdural and epidural field potentials (EFPs). APPROACH We measured the accuracy of decoding continuous hand and finger kinematics during naturalistic grasping motions in five human subjects. We recorded subdural surface potentials (electrocorticography; ECoG) as well as with EFPs, with both standard- and high-resolution electrode arrays. MAIN RESULTS In all five subjects, decoding of continuous kinematics significantly exceeded chance, using either EGoG or EFPs. ECoG decoding accuracy compared favorably with prior investigations of grasp kinematics (mean ± SD grasp aperture variance accounted for was 0.54 ± 0.05 across all subjects, 0.75 ± 0.09 for the best subject). In general, EFP decoding performed comparably to ECoG decoding. The 7-20 Hz and 70-115 Hz spectral bands contained the most information about grasp kinematics, with the 70-115 Hz band containing greater information about more subtle movements. Higher-resolution recording arrays provided clearly superior performance compared to standard-resolution arrays. SIGNIFICANCE To approach the fine motor control achieved by an intact brain-body system, it will be necessary to execute motor intent on a continuous basis with high accuracy. The current results demonstrate that this level of accuracy might be achievable not just with ECoG, but with EFPs as well. Epidural placement of electrodes is less invasive, and therefore may incur less risk of encephalitis or stroke than subdural placement of electrodes. Accurately decoding motor commands at the epidural level may be an important step towards a clinically viable brain-machine interface.

Potential of human dental stem cells in repairing the complete transection of rat spinal cord.

Abstract: Objective The adult spinal cord of mammals contains a certain amount of neural precursor cells, but these endogenous cells have a limited capacity for replacement of lost cells after spinal cord injury. The exogenous stem cells transplantation has become a therapeutic strategy for spinal cord repairing because of their immunomodulatory and differentiation capacity. In addition, dental stem cells originating from the cranial neural crest might be candidate cell sources for neural engineering. Approach Human dental follicle stem cells (DFSCs), stem cells from apical papilla (SCAPs) and dental pulp stem cells (DPSCs) were isolated and identified in vitro, then green GFP-labeled stem cells with pellets were transplanted into completely transected spinal cord. The functional recovery of rats and multiple neuro-regenerative mechanisms were explored. Main results The dental stem cells, especially DFSCs, demonstrated the potential in repairing the completely transected spinal cord and promote functional recovery after injury. The major involved mechanisms were speculated below: First, dental stem cells inhibited the expression of interleukin-1β to reduce the inflammatory response; second, they inhibited the expression of ras homolog gene family member A (RhoA) to promote neurite regeneration; third, they inhibited the sulfonylurea receptor1 (SUR-1) expression to reduce progressive hemorrhagic necrosis; lastly, parts of the transplanted cells survived and differentiated into mature neurons and oligodendrocytes but not astrocyte, which is beneficial for promoting axons growth. Significance Dental stem cells presented remarkable tissue regenerative capability after spinal cord injury through immunomodulatory, differentiation and protection capacity.

Comparison of connectivity analyses for resting state EEG data.

Abstract: Objective In the present work, a nonlinear measure (transfer entropy, TE) was used in a multivariate approach for the analysis of effective connectivity in high density resting state EEG data in eyes open and eyes closed. Advantages of the multivariate approach in comparison to the bivariate one were tested. Moreover, the multivariate TE was compared to an effective linear measure, i.e. directed transfer function (DTF). Finally, the existence of a relationship between the information transfer and the level of brain synchronization as measured by phase synchronization value (PLV) was investigated. Approach The comparison between the connectivity measures, i.e. bivariate versus multivariate TE, TE versus DTF, TE versus PLV, was performed by means of statistical analysis of indexes based on graph theory. Main results The multivariate approach is less sensitive to false indirect connections with respect to the bivariate estimates. The multivariate TE differentiated better between eyes closed and eyes open conditions compared to DTF. Moreover, the multivariate TE evidenced non-linear phenomena in information transfer, which are not evidenced by the use of DTF. We also showed that the target of information flow, in particular the frontal region, is an area of greater brain synchronization. Significance Comparison of different connectivity analysis methods pointed to the advantages of nonlinear methods, and indicated a relationship existing between the flow of information and the level of synchronization of the brain.

Modeling the response of small myelinated axons in a compound nerve to kilohertz frequency signals

Abstract: Objective. There is growing interest in electrical neuromodulation of peripheral nerves, particularly autonomic nerves, to treat various diseases. Electrical signals in the kilohertz frequency (KHF) range can produce different responses, including conduction block. For example, EnteroMedics' vBloc® therapy for obesity delivers 5 kHz stimulation to block the abdominal vagus nerves, but the mechanisms of action are unclear. Approach. We developed a two-part computational model, coupling a 3D finite element model of a cuff electrode around the human abdominal vagus nerve with biophysically-realistic electrical circuit equivalent (cable) model axons (1, 2, and 5.7 µm in diameter). We developed an automated algorithm to classify conduction responses as subthreshold (transmission), KHF-evoked activity (excitation), or block. We quantified neural responses across kilohertz frequencies (5–20 kHz), amplitudes (1–8 mA), and electrode designs. Main results. We found heterogeneous conduction responses across the modeled nerve trunk, both for a given parameter set and across parameter sets, although most suprathreshold responses were excitation, rather than block. The firing patterns were irregular near transmission and block boundaries, but otherwise regular, and mean firing rates varied with electrode-fibre distance. Further, we identified excitation responses at amplitudes above block threshold, termed 're-excitation', arising from action potentials initiated at virtual cathodes. Excitation and block thresholds decreased with smaller electrode-fibre distances, larger fibre diameters, and lower kilohertz frequencies. A point source model predicted a larger fraction of blocked fibres and greater change of threshold with distance as compared to the realistic cuff and nerve model. Significance. Our findings of widespread asynchronous KHF-evoked activity suggest that conduction block in the abdominal vagus nerves is unlikely with current clinical parameters. Our results indicate that compound neural or downstream muscle force recordings may be unreliable as quantitative measures of neural activity for in vivo studies or as biomarkers in closed-loop clinical devices.

Intracranial EEG fluctuates over months after implanting electrodes in human brain.

Abstract: Objective Implanting subdural and penetrating electrodes in the brain causes acute trauma and inflammation that affect intracranial electroencephalographic (iEEG) recordings. This behavior and its potential impact on clinical decision-making and algorithms for implanted devices have not been assessed in detail. In this study we aim to characterize the temporal and spatial variability of continuous, prolonged human iEEG recordings. Approach Intracranial electroencephalography from 15 patients with drug-refractory epilepsy, each implanted with 16 subdural electrodes and continuously monitored for an average of 18 months, was included in this study. Time and spectral domain features were computed each day for each channel for the duration of each patient's recording. Metrics to capture post-implantation feature changes and inflexion points were computed on group and individual levels. A linear mixed model was used to characterize transient group-level changes in feature values post-implantation and independent linear models were used to describe individual variability. Main results A significant decline in features important to seizure detection and prediction algorithms (mean line length, energy, and half-wave), as well as mean power in the Berger and high gamma bands, was observed in many patients over 100 d following implantation. In addition, spatial variability across electrodes declines post-implantation following a similar timeframe. All selected features decreased by 14-50% in the initial 75 d of recording on the group level, and at least one feature demonstrated this pattern in 13 of the 15 patients. Our findings indicate that iEEG signal features demonstrate increased variability following implantation, most notably in the weeks immediately post-implant. Significance These findings suggest that conclusions drawn from iEEG, both clinically and for research, should account for spatiotemporal signal variability and that properly assessing the iEEG in patients, depending upon the application, may require extended monitoring.

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

Abstract: 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

A hybrid BMI-based exoskeleton for paresis: EMG control for assisting arm movements.

Abstract: Objective. Brain–machine interface (BMI) technologies have succeeded in controlling robotic exoskeletons, enabling some paralyzed people to control their own arms and hands. We have developed an exoskeleton asynchronously controlled by EEG signals. In this study, to enable real-time control of the exoskeleton for paresis, we developed a hybrid system with EEG and EMG signals, and the EMG signals were used to estimate its joint angles. Approach. Eleven able-bodied subjects and two patients with upper cervical spinal cord injuries (SCIs) performed hand and arm movements, and the angles of the metacarpophalangeal (MP) joint of the index finger, wrist, and elbow were estimated from EMG signals using a formula that we derived to calculate joint angles from EMG signals, based on a musculoskeletal model. The formula was exploited to control the elbow of the exoskeleton after automatic adjustments. Four able-bodied subjects and a patient with upper cervical SCI wore an exoskeleton controlled using EMG signals and were required to perform hand and arm movements to carry and release a ball. Main results. Estimated angles of the MP joints of index fingers, wrists, and elbows were correlated well with the measured angles in 11 able-bodied subjects (correlation coefficients were 0.81 ± 0.09, 0.85 ± 0.09, and 0.76 ± 0.13, respectively) and the patients (e.g. 0.91 ± 0.01 in the elbow of a patient). Four able-bodied subjects successfully positioned their arms to adequate angles by extending their elbows and a joint of the exoskeleton, with root-mean-square errors <6°. An upper cervical SCI patient, empowered by the exoskeleton, successfully carried a ball to a goal in all 10 trials. Significance. A BMI-based exoskeleton for paralyzed arms and hands using real-time control was realized by designing a new method to estimate joint angles based on EMG signals, and these may be useful for practical rehabilitation and the support of daily actions.

Design and manufacturing challenges of optogenetic neural interfaces: a review

Abstract: S B Goncalves is fully supported by the Portuguese Foundation for Science and Technology (FCT) under grant PD/BD/105931/2014 from MIT Portugal program. This work is funded by the FCT with the reference project UID/EEA/04436/2013, by FEDER funds through COMPETE 2020—Programa Operacional Competitividade e Internacionalizaccao (POCI) with the reference project POCI- 01-0145-FEDER-006941. This work is also funded by ANI through the Brain-Lighting project by FEDER funds through Portugal 2020, COMPETE 2020 with the reference project POCI-01-0247-FEDER-003416. Moreover, this work is supported by ITEC - Iberiana Technical, Lda (Braga, Portugal).

Particle swarm optimization for programming deep brain stimulation arrays

Abstract: Objective. Deep brain stimulation (DBS) therapy relies on both precise neurosurgical targeting and systematic optimization of stimulation settings to achieve beneficial clinical outcomes. One recent advance to improve targeting is the development of DBS arrays (DBSAs) with electrodes segmented both along and around the DBS lead. However, increasing the number of independent electrodes creates the logistical challenge of optimizing stimulation parameters efficiently. Approach. Solving such complex problems with multiple solutions and objectives is well known to occur in biology, in which complex collective behaviors emerge out of swarms of individual organisms engaged in learning through social interactions. Here, we developed a particle swarm optimization (PSO) algorithm to program DBSAs using a swarm of individual particles representing electrode configurations and stimulation amplitudes. Using a finite element model of motor thalamic DBS, we demonstrate how the PSO algorithm can efficiently optimize a multi-objective function that maximizes predictions of axonal activation in regions of interest (ROI, cerebellar-receiving area of motor thalamus), minimizes predictions of axonal activation in regions of avoidance (ROA, somatosensory thalamus), and minimizes power consumption. Main results. The algorithm solved the multi-objective problem by producing a Pareto front. ROI and ROA activation predictions were consistent across swarms (<1% median discrepancy in axon activation). The algorithm was able to accommodate for (1) lead displacement (1 mm) with relatively small ROI (≤9.2%) and ROA (≤1%) activation changes, irrespective of shift direction; (2) reduction in maximum per-electrode current (by 50% and 80%) with ROI activation decreasing by 5.6% and 16%, respectively; and (3) disabling electrodes (n = 3 and 12) with ROI activation reduction by 1.8% and 14%, respectively. Additionally, comparison between PSO predictions and multi-compartment axon model simulations showed discrepancies of <1% between approaches. Significance. The PSO algorithm provides a computationally efficient way to program DBS systems especially those with higher electrode counts.

An improved P300 pattern in BCI to catch user’s attention

Abstract: Objective. Brain-computer interfaces (BCIs) can help patients who have lost control over most muscles but are still conscious and able to communicate or interact with the environment. One of the most popular types of BCI is the P300-based BCI. With this BCI, users are asked to count the number of appearances of target stimuli in an experiment. To date, the majority of visual P300-based BCI systems developed have used the same character or picture as the target for every stimulus presentation, which can bore users. Consequently, users attention may decrease or be negatively affected by adjacent stimuli. Approach. In this study, a new stimulus is presented to increase user concentration. Honeycomb-shaped figures with 1–3 red dots were used as stimuli. The number and the positions of the red dots in the honeycomb-shaped figure were randomly changed during BCI control. The user was asked to count the number of the dots presented in each flash instead of the number of times they flashed. To assess the performance of this new stimulus, another honeycomb-shaped stimulus, without red dots, was used as a control condition. Main results. The results showed that the honeycomb-shaped stimuli with red dots obtained significantly higher classification accuracies and information transfer rates (p < 0.05) compared to the honeycomb-shaped stimulus without red dots. Significance. The results indicate that this proposed method can be a promising approach to improve the performance of the BCI system and can be an efficient method in daily application.

A novel flexible cuff-like microelectrode for dual purpose, acute and chronic electrical interfacing with the mouse cervical vagus nerve

Abstract: Objective. Neural reflexes regulate immune responses and homeostasis. Advances in bioelectronic medicine indicate that electrical stimulation of the vagus nerve can be used to treat inflammatory disease, yet the understanding of neural signals that regulate inflammation is incomplete. Current interfaces with the vagus nerve do not permit effective chronic stimulation or recording in mouse models, which is vital to studying the molecular and neurophysiological mechanisms that control inflammation homeostasis in health and disease. We developed an implantable, dual purpose, multi-channel, flexible 'microelectrode' array, for recording and stimulation of the mouse vagus nerve. Approach. The array was microfabricated on an 8 µm layer of highly biocompatible parylene configured with 16 sites. The microelectrode was evaluated by studying the recording and stimulation performance. Mice were chronically implanted with devices for up to 12 weeks. Main results. Using the microelectrode in vivo, high fidelity signals were recorded during physiological challenges (e.g potassium chloride and interleukin-1β), and electrical stimulation of the vagus nerve produced the expected significant reduction of blood levels of tumor necrosis factor (TNF) in endotoxemia. Inflammatory cell infiltration at the microelectrode 12 weeks of implantation was limited according to radial distribution analysis of inflammatory cells. Significance. This novel device provides an important step towards a viable chronic interface for cervical vagus nerve stimulation and recording in mice.

Classification of different reaching movements from the same limb using EEG.

Abstract: Objective. Brain–computer-interfaces (BCIs) have been proposed not only as assistive technologies but also as rehabilitation tools for lost functions. However, due to the stochastic nature, poor spatial resolution and signal to noise ratio from electroencephalography (EEG), multidimensional decoding has been the main obstacle to implement non-invasive BCIs in real-live rehabilitation scenarios. This study explores the classification of several functional reaching movements from the same limb using EEG oscillations in order to create a more versatile BCI for rehabilitation. Approach. Nine healthy participants performed four 3D center-out reaching tasks in four different sessions while wearing a passive robotic exoskeleton at their right upper limb. Kinematics data were acquired from the robotic exoskeleton. Multiclass extensions of Filter Bank Common Spatial Patterns (FBCSP) and a linear discriminant analysis (LDA) classifier were used to classify the EEG activity into four forward reaching movements (from a starting position towards four target positions), a backward movement (from any of the targets to the starting position and rest). Recalibrating the classifier using data from previous or the same session was also investigated and compared. Main results. Average EEG decoding accuracy were significantly above chance with 67%, 62.75%, and 50.3% when decoding three, four and six tasks from the same limb, respectively. Furthermore, classification accuracy could be increased when using data from the beginning of each session as training data to recalibrate the classifier. Significance. Our results demonstrate that classification from several functional movements performed by the same limb is possible with acceptable accuracy using EEG oscillations, especially if data from the same session are used to recalibrate the classifier. Therefore, an ecologically valid decoding could be used to control assistive or rehabilitation mutli-degrees of freedom (DoF) robotic devices using EEG data. These results have important implications towards assistive and rehabilitative neuroprostheses control in paralyzed patients.

A micro-scale printable nanoclip for electrical stimulation and recording in small nerves

Abstract: Objective. The vision of bioelectronic medicine is to treat disease by modulating the signaling of visceral nerves near various end organs. In small animal models, the nerves of interest can have small diameters and limited surgical access. New high-resolution methods for building nerve interfaces are desirable. In this study, we present a novel nerve interface and demonstrate its use for stimulation and recording in small nerves. Approach. We design and fabricate micro-scale electrode-laden nanoclips capable of interfacing with nerves as small as 50 µm in diameter. The nanoclips are fabricated using a direct laser writing technique with a resolution of 200 nm. The resolution of the printing process allows for incorporation of a number of innovations such as trapdoors to secure the device to the nerve, and quick-release mounts that facilitate keyhole surgery, obviating the need for forceps. The nanoclip can be built around various electrode materials; here we use carbon nanotube fibers for minimally invasive tethering. Main results. We present data from stimulation-evoked responses of the tracheal syringeal (hypoglossal) nerve of the zebra finch, as well as quantification of nerve functionality at various time points post implant, demonstrating that the nanoclip is compatible with healthy nerve activity over sub-chronic timescales. Significance. Our nerve interface addresses key challenges in interfacing with small nerves in the peripheral nervous system. Its small size, ability to remain on the nerve over sub-chronic timescales, and ease of implantation, make it a promising tool for future use in the treatment of disease.

A silicon carbide array for electrocorticography and peripheral nerve recording.

Abstract: Objective. Current neural probes have a limited device lifetime of a few years. Their common failure mode is the degradation of insulating films and/or the delamination of the conductor–insulator interfaces. We sought to develop a technology that does not suffer from such limitations and would be suitable for chronic applications with very long device lifetimes. Approach. We developed a fabrication method that integrates polycrystalline conductive silicon carbide with insulating silicon carbide. The technology employs amorphous silicon carbide as the insulator and conductive silicon carbide at the recording sites, resulting in a seamless transition between doped and amorphous regions of the same material, eliminating heterogeneous interfaces prone to delamination. Silicon carbide has outstanding chemical stability, is biocompatible, is an excellent molecular barrier and is compatible with standard microfabrication processes. Main results. We have fabricated silicon carbide electrode arrays using our novel fabrication method. We conducted in vivo experiments in which electrocorticography recordings from the primary visual cortex of a rat were obtained and were of similar quality to those of polymer based electrocorticography arrays. The silicon carbide electrode arrays were also used as a cuff electrode wrapped around the sciatic nerve of a rat to record the nerve response to electrical stimulation. Finally, we demonstrated the outstanding long term stability of our insulating silicon carbide films through accelerated aging tests. Significance. Clinical translation in neural engineering has been slowed in part due to the poor long term performance of current probes. Silicon carbide devices are a promising technology that may accelerate this transition by enabling truly chronic applications.

Electron transfer processes occurring on platinum neural stimulating electrodes: calculated charge-storage capacities are inaccessible during applied stimulation.

Abstract: Objective Neural prostheses employing platinum electrodes are often constrained by a charge/charge-density parameter known as the Shannon limit. In examining the relationship between charge injection and observed tissue damage, the electrochemistry at the electrode-tissue interface should be considered. The charge-storage capacity (CSC) is often used as a predictor of how much charge an electrode can inject during stimulation, but calculating charge from a steady-state i-E curve (cyclic voltammogram) over the water window misrepresents how electrodes operate during stimulation. We aim to gain insight into why CSC predictions from classic i-E curves overestimate the amount of charge that can be injected during neural stimulation pulsing. Approach In this study, we use a standard electrochemical technique to investigate how platinum electrochemistry depends on the potentials accessed by the electrode and on the electrolyte composition. Main results The experiments indicate: (1) platinum electrodes must be subjected to a 'cleaning' procedure in order to expose the maximum number of surface platinum sites for hydrogen adsorption; (2) the 'cleaned' platinum surface will likely revert to an obstructed condition under typical neural stimulation conditions; (3) irreversible oxygen reduction may occur under neural stimulation conditions, so the consequences of this reaction should be considered; and (4) the presence of the chloride ion (Cl-) or proteins (bovine serum albumin) inhibits oxide formation and alters H adsorption. Significance These observations help explain why traditional CSC calculations overestimate the charge that can be injected during neural stimulation. The results underscore how careful electrochemical examination of the electrode-electrolyte interface can result in more accurate expectations of electrode performance during applied stimulation.

Enhancing performance of a motor imagery based brain–computer interface by incorporating electrical stimulation-induced SSSEP

Abstract: Objective. We proposed a novel simultaneous hybrid brain–computer interface (BCI) by incorporating electrical stimulation into a motor imagery (MI) based BCI system. The goal of this study was to enhance the overall performance of an MI-based BCI. In addition, the brain oscillatory pattern in the hybrid task was also investigated. Approach. 64-channel electroencephalographic (EEG) data were recorded during MI, selective attention (SA) and hybrid tasks in fourteen healthy subjects. In the hybrid task, subjects performed MI with electrical stimulation which was applied to bilateral median nerve on wrists simultaneously. Main results. The hybrid task clearly presented additional steady-state somatosensory evoked potential (SSSEP) induced by electrical stimulation with MI-induced event-related desynchronization (ERD). By combining ERD and SSSEP features, the performance in the hybrid task was significantly better than in both MI and SA tasks, achieving a ~14% improvement in total relative to the MI task alone and reaching ~89% in mean classification accuracy. On the contrary, there was no significant enhancement obtained in performance while separate ERD feature was utilized in the hybrid task. In terms of the hybrid task, the performance using combined feature was significantly better than using separate ERD or SSSEP feature. Significance. The results in this work validate the feasibility of our proposed approach to form a novel MI-SSSEP hybrid BCI outperforming a conventional MI-based BCI through combing MI with electrical stimulation.

Neural control of finger movement via intracortical brain-machine interface

Abstract: Objective Intracortical brain-machine interfaces (BMIs) are a promising source of prosthesis control signals for individuals with severe motor disabilities. Previous BMI studies have primarily focused on predicting and controlling whole-arm movements; precise control of hand kinematics, however, has not been fully demonstrated. Here, we investigate the continuous decoding of precise finger movements in rhesus macaques. Approach In order to elicit precise and repeatable finger movements, we have developed a novel behavioral task paradigm which requires the subject to acquire virtual fingertip position targets. In the physical control condition, four rhesus macaques performed this task by moving all four fingers together in order to acquire a single target. This movement was equivalent to controlling the aperture of a power grasp. During this task performance, we recorded neural spikes from intracortical electrode arrays in primary motor cortex. Main results Using a standard Kalman filter, we could reconstruct continuous finger movement offline with an average correlation of ρ = 0.78 between actual and predicted position across four rhesus macaques. For two of the monkeys, this movement prediction was performed in real-time to enable direct brain control of the virtual hand. Compared to physical control, neural control performance was slightly degraded; however, the monkeys were still able to successfully perform the task with an average target acquisition rate of 83.1%. The monkeys' ability to arbitrarily specify fingertip position was also quantified using an information throughput metric. During brain control task performance, the monkeys achieved an average 1.01 bits s-1 throughput, similar to that achieved in previous studies which decoded upper-arm movements to control computer cursors using a standard Kalman filter. Significance This is, to our knowledge, the first demonstration of brain control of finger-level fine motor skills. We believe that these results represent an important step towards full and dexterous control of neural prosthetic devices.