Journal of Neural Engineering (J Neural Eng)

Publisher: Institute of Physics (Great Britain), IOP Publishing

Journal description

Journal of Neural Engineering is a new forum for the interdisciplinary field of neural engineering, where neuroscientists, neurobiologists and engineers can publish their work in one periodical that bridges the gap between neuroscience and engineering. Articles will cover the field of neural engineering at the molecular, cellular and systems levels.

Current impact factor: 3.30

Impact Factor Rankings

2016 Impact Factor Available summer 2017
2014 / 2015 Impact Factor 3.295
2013 Impact Factor 3.415
2012 Impact Factor 3.282
2011 Impact Factor 3.837
2010 Impact Factor 2.628
2009 Impact Factor 3.739
2008 Impact Factor 2.737

Impact factor over time

Impact factor

Additional details

5-year impact 3.74
Cited half-life 3.90
Immediacy index 0.75
Eigenfactor 0.01
Article influence 1.08
Website Journal of Neural Engineering website
Other titles Journal of neural engineering (Online), Neural engineering, JNE
ISSN 1741-2552
OCLC 54314172
Material type Document, Periodical, Internet resource
Document type Internet Resource, Computer File, Journal / Magazine / Newspaper

Publisher details

IOP Publishing

  • Pre-print
    • Author can archive a pre-print version
  • Post-print
    • Author can archive a post-print version
  • Conditions
    • Pre-print on author's personal website, repository or arXiv.
    • Pre-print can not be updated after submission
    • Post-print on author's personal website immediately
    • Post-print on institutional repository, subject-based repository, PubMed Central or third party eprint servers after 12 months embargo
    • Publisher's version/PDF cannot be used
    • Published source must be acknowledged with citation
    • Must link to publisher version with DOI
    • Set statements to accompany different versions (see policy)
    • This policy is an exception to the default policies of 'IOP Publishing'
  • Classification

Publications in this journal

  • [Show abstract] [Hide abstract]
    ABSTRACT: Objective: The brain characteristics of different people are not the same. Brain computer interfaces (BCIs) should thus be customized for each individual person. In motor-imagery based synchronous BCIs, a number of parameters (referred to as hyper-parameters) including the EEG frequency bands, the channels and the time intervals from which the features are extracted should be pre-determined based on each subject's brain characteristics. Approach: To determine the hyper-parameter values, previous work has relied on manual or semi-automatic methods that are not applicable to high-dimensional search spaces. In this paper, we propose a fully automatic, scalable and computationally inexpensive algorithm that uses Bayesian optimization to tune these hyper-parameters. We then build different classifiers trained on the sets of hyper-parameter values proposed by the Bayesian optimization. A final classifier aggregates the results of the different classifiers. Main results: We have applied our method to 21 subjects from three BCI competition datasets. We have conducted rigorous statistical tests, and have shown the positive impact of hyper-parameter optimization in improving the accuracy of BCIs. Furthermore, We have compared our results to those reported in the literature. Significance: Unlike the best reported results in the literature, which are based on more sophisticated feature extraction and classification methods, and rely on prestudies to determine the hyper-parameter values, our method has the advantage of being fully automated, uses less sophisticated feature extraction and classification methods, and yields similar or superior results compared to the best performing designs in the literature.
    No preview · Article · Jan 2016 · Journal of Neural Engineering
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    ABSTRACT: Objective: One of the major drawbacks in EEG brain-computer interfaces (BCI) is the need for subject-specific training of the classifier. By removing the need for a supervised calibration phase, new users could potentially explore a BCI faster. In this work we aim to remove this subject-specific calibration phase and allow direct classification. Approach: We explore canonical polyadic decompositions and block term decompositions of the EEG. These methods exploit structure in higher dimensional data arrays called tensors. The BCI tensors are constructed by concatenating ERP templates from other subjects to a target and non-target trial and the inherent structure guides a decomposition that allows accurate classification. We illustrate the new method on data from a three-class auditory oddball paradigm. Main results: The presented approach leads to a fast and intuitive classification with accuracies competitive with a supervised and cross-validated LDA approach. Significance: The described methods are a promising new way of classifying BCI data with a forthright link to the original P300 ERP signal over the conventional and widely used supervised approaches.
    No preview · Article · Jan 2016 · Journal of Neural Engineering
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    ABSTRACT: Objective: Patients with amyotrophic lateral sclerosis (ALS) may benefit from brain-computer interfaces (BCI), but the utility of such devices likely will have to account for the functional, cognitive, and behavioral heterogeneity of this neurodegenerative disorder. Approach: In this study, a heterogeneous group of patients with ALS participated in a study on BCI based on the P300 event related potential and motor-imagery. Results: The presence of cognitive impairment in these patients significantly reduced the quality of the control signals required to use these communication systems, subsequently impairing performance, regardless of progression of physical symptoms. Loss in performance among the cognitively impaired was accompanied by a decrease in the signal-to-noise ratio of task-relevant EEG band power. There was also evidence that behavioral dysfunction negatively affects P300 speller performance. Finally, older participants achieved better performance on the P300 system than the motor-imagery system, indicating a preference of BCI paradigm with age. Significance: These findings highlight the importance of considering the heterogeneity of disease when designing BCI augmentative and alternative communication devices for clinical applications.
    No preview · Article · Jan 2016 · Journal of Neural Engineering
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    ABSTRACT: Objective: Medical implants made of non-biological materials provoke a chronic inflammatory response, resulting in the deposition of a collagenous scar tissue (ST) layer on their surface, that gradually thickens over time. This is a critical problem for neural interfaces. Scar build-up on electrodes results in a progressive decline in signal level because the scar tissue gradually separates axons away from the recording contacts. In regenerative sieves and microchannel electrodes, progressive scar deposition will constrict and may eventually choke off the sieve hole or channel lumen. Interface designs need to address this issue if they are to be fit for long term use. This study examines a novel method of inhibiting the formation and thickening of the fibrous scar. Approach: Research to date has mainly focused on methods of preventing stimulation of the foreign body response by implant surface modification. In this paper a pharmacological approach using drug elution to suppress chronic inflammation is introduced. Microchannel implants made of silicone doped with the steroid drug dexamethasone were implanted in the rat sciatic nerve for periods of up to a year. Tissue from within the microchannels was compared to that from control devices that did not release any drug. Main results: In the drug eluting implants the scar layer was significantly thinner at all timepoints, and unlike the controls it did not continue to thicken after 6 months. Control implants supported axon regeneration well initially, but axon counts fell rapidly at later timepoints as scar thickened. Axon counts in drug eluting devices were initially much lower, but increased rather than declined and by one year were significantly higher than in controls. Significance: Drug elution offers a potential long term solution to the problem of performance degradation due to scarring around neural implants.
    No preview · Article · Jan 2016 · Journal of Neural Engineering
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    ABSTRACT: Objective: Recently, several studies have documented the presence of a bimodal distribution of spike waveform widths in primary motor cortex. Although narrow and wide spiking neurons, corresponding to the two modes of the distribution, exhibit different response properties, it remains unknown if these differences give rise to differential decoding performance between these two classes of cells. Approach: We used a Gaussian mixture model to classify neurons into narrow and wide physiological classes. Using similar-size, random samples of neurons from these two physiological classes, we trained offline decoding models to predict a variety of movement features. We compared offline decoding performance between these two physiologically defined populations of cells. Main results: We found that narrow spiking neural ensembles decode motor parameters better than wide spiking neural ensembles including kinematics, kinetics, and muscle activity. Significance: These findings suggest that the utility of neural ensembles in brain machine interfaces may be predicted from their spike waveform widths.
    No preview · Article · Jan 2016 · Journal of Neural Engineering
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    ABSTRACT: Objective: Signal attenuation is a major problem facing intracortical sensors for chronic neuroprosthetic applications. Many studies suggest that failure is due to gliosis around the electrode tips, however, mechanical and material causes of failure are often overlooked. The purpose of this study was to investigate the factors contributing to progressive signal decline by using scanning electron microscopy (SEM) to visualize structural changes in chronically implanted arrays and histology to examine the tissue response at corresponding implant sites. Approach: We examined eight chronically implanted intracortical microelectrode arrays (MEAs) explanted from non-human primates at times ranging from 37 to 1051 days post-implant. We used SEM, in vivo neural recordings, and histology (GFAP, Iba-1, NeuN). Three MEAs that were never implanted were also imaged as controls. Main results: SEM revealed progressive corrosion of the platinum electrode tips and changes to the underlying silicon. The parylene insulation was prone to cracking and delamination, and in some instances the silicone elastomer also delaminated from the edges of the MEA. Substantial tissue encapsulation was observed and was often seen growing into defects in the platinum and parylene. These material defects became more common as the time in vivo increased. Histology at 37 and 1051 days post-implant showed gliosis, disruption of normal cortical architecture with minimal neuronal loss, and high Iba-1 reactivity, especially within the arachnoid and dura. Electrode tracts were either absent or barely visible in the cortex at 1051 days, but were seen in the fibrotic encapsulation material suggesting that the MEAs were lifted out of the brain. Neural recordings showed a progressive drop in impedance, signal amplitude, and viable channels over time. Significance: These results provide evidence that signal loss in MEAs is truly multifactorial. Gliosis occurs in the first few months after implantation but does not prevent useful recordings for several years. Progressive meningeal fibrosis encapsulates and lifts MEAs out of the cortex while ongoing foreign body reactions lead to progressive degradation of the materials. Long-term impedance drops are due to the corrosion of platinum, cracking and delamination of parylene, and delamination of silicone elastomer. Oxygen radicals released by cells of the immune system likely mediate the degradation of these materials. Future MEA designs must address these problems through more durable insulation materials, more inert electrode alloys, and pharmacologic suppression of fibroblasts and leukocytes.
    No preview · Article · Jan 2016 · Journal of Neural Engineering
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    ABSTRACT: Main results: While endovascular signals were known to be larger in amplitude than scalp signals, our analysis in rabbits borrows a standard technique from communication theory to show endovascular signals also have up to 100× better signal-to-noise ratio. Significance: With a viable minimally-invasive path to high-quality brain signals, patients with brain diseases could one day receive potent electroceuticals through the bloodstream, in the course of a brief outpatient procedure.
    No preview · Article · Jan 2016 · Journal of Neural Engineering
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    ABSTRACT: Objective: State-of-the-art experiments for studying neural processes underlying visual cognition often constrain sensory inputs (e.g., static images) and our behavior (e.g., fixed eye-gaze, long eye fixations), isolating or simplifying the interaction of neural processes. Motivated by the non-stationarity of our natural visual environment, we investigated the electroencephalography (EEG) correlates of visual recognition while participants overtly performed visual search in non-stationary scenes. We hypothesized that visual effects (such as those typically used in human-computer interfaces) may increase temporal uncertainty (with reference to fixation onset) of cognition-related EEG activity in an active search task and therefore require novel techniques for single-trial detection. Approach: We addressed fixation-related EEG activity in an active search task with respect to stimulus-appearance styles and dynamics. Alongside popping-up stimuli, our experimental study embraces two composite appearance styles based on fading-in, enlarging, and motion effects. Additionally, we explored whether the knowledge obtained in the pop-up experimental setting can be exploited to boost the EEG-based intention-decoding performance when facing transitional changes of visual content. Main results: The results confirmed our initial hypothesis that the dynamic of visual content can increase temporal uncertainty of the cognition-related EEG activity in active search with respect to fixation onset. This temporal uncertainty challenges the pivotal aim to keep the decoding performance constant irrespective of visual effects. Importantly, the proposed approach for EEG decoding based on knowledge transfer between the different experimental settings gave a promising performance. Significance: Our study demonstrates that the non-stationarity of visual scenes is an important factor in the evolution of cognitive processes, as well as in the dynamic of ocular behavior (i.e., dwell time and fixation duration) in an active search task. In addition, our method to improve single-trial detection performance in this adverse scenario is an important step in making brain-computer interfacing technology available for human-computer interaction applications.
    Full-text · Article · Jan 2016 · Journal of Neural Engineering
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    ABSTRACT: Objective: Functional magnetic resonance imaging blood oxygen level dependent (BOLD) determinations of correlations between 'resting-state' neuronal activity in different regions of cortex have generated much interest. Determination of these correlations requires regressing out signals that are correlated in all parts of the cortex and are taken to be artefactual, such as those due to movement, respiration and cardiovascular activity. However when these are removed there still remains a 'global signal' (GS), which is taken to be of unknown physiological origin, and is regressed out by some researchers but not by others. Approach: We have investigated the origin of this GS using cortical models consisting of coupled networks of modules representing regions of interest. Main results: We show that the GS has an amplitude that is linearly related to the average correlation between the modules/voxels in the network over a large range of such correlations. The GS arises as a consequence of feedback between the modules/voxels leading to correlations in their BOLD signals. Given the relationship between the GS and the average correlations it might be anticipated that regressing out the GS during preprocessing will significantly modify the correlations subsequently determined. This is shown to be the case when comparing the connections of individual modules with that predicted by the correlations. Significance: The present model shows that such correlations can arise as a consequence of the intermodular feedback connectivity without recourse to imposing a GS independent of the connectivity. Our model indicates that the GS reflects the extent of feedback pathways provided by the intermodular/inter-regional connections and hence the average correlation between modules or regions of cortex. However the model has not been used to elucidate the possible contributions of a GS independent of the connectivity, which might indeed contribute to the GS of the cortex.
    No preview · Article · Dec 2015 · Journal of Neural Engineering
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    ABSTRACT: Objective: We characterized electrode stability over twelve weeks of impedance and neural recording data from four chronically-implanted Utah arrays in two rhesus macaques, and investigated the effects of glial scarring and interface interactions at the electrode recording site on signal quality using a computational model. Approach: A finite-element model of a Utah array microelectrode in neural tissue was coupled with a multi-compartmental model of a neuron to quantify the effects of encapsulation thickness, encapsulation resistivity, and interface resistivity on electrode impedance and waveform amplitude. The coupled model was then reconciled with the in vivo data. Histology was obtained seventeen weeks post-implantation to measure gliosis. Main results: From week 1-3, mean impedance and amplitude increased at rates of 115.8 kΩ/week and 23.1 μV/week, respectively. This initial ramp up in impedance and amplitude was observed across all arrays, and is consistent with biofouling (increasing interface resistivity) and edema clearing (increasing tissue resistivity), respectively, in the model. Beyond week 3, the trends leveled out. Histology showed that thin scars formed around the electrodes. In the model, scarring could not match the in vivo data. However, a thin interface layer at the electrode tip could. Despite having a large effect on impedance, interface resistivity did not have a noticeable effect on amplitude. Significance: This study suggests that scarring does not cause an electrical problem with regard to signal quality since it does not appear to be the main contributor to increasing impedance or significantly affect amplitude unless it displaces neurons. This, in turn, suggests that neural signals can be obtained reliably despite scarring as long as the recording site has sufficiently low impedance after accumulating a thin layer of biofouling. Therefore, advancements in microelectrode technology may be expedited by focusing on improvements to the recording site-tissue interface rather than elimination of the glial scar.
    Preview · Article · Dec 2015 · Journal of Neural Engineering
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    ABSTRACT: Objective: Tactile feedback is critical to grip and object manipulation. Its absence results in reliance on visual and auditory cues. Our objective was to assess the effect of sensory feedback on task performance in individuals with limb loss. Approach: Stimulation of the peripheral nerves using implanted cuff electrodes provided two subjects with sensory feedback with intensity proportional to forces on the thumb, index, and middle fingers of their prosthetic hand during object manipulation. Both subjects perceived the sensation on their phantom hand at locations corresponding to the locations of the forces on the prosthetic hand. A bend sensor measured prosthetic hand span. Hand span modulated the intensity of sensory feedback perceived on the thenar eminence for subject 1 and the middle finger for subject 2. We performed three functional tests with the blindfolded subjects. First, the subject tried to determine whether or not a wooden block had been placed in his prosthetic hand. Second, the subject had to locate and remove magnetic blocks from a metal table. Third, the subject performed the Southampton Hand Assessment Procedure (SHAP). We also measured the subject's sense of embodiment with a survey and his self-confidence. Main results: Blindfolded performance with sensory feedback was similar to sighted performance in the wooden block and magnetic block tasks. Performance on the SHAP, a measure of hand mechanical function and control, was similar with and without sensory feedback. An embodiment survey showed an improved sense of integration of the prosthesis in self body image with sensory feedback. Significance: Sensory feedback by peripheral nerve stimulation improved object discrimination and manipulation, embodiment, and confidence. With both forms of feedback, the blindfolded subjects tended toward results obtained with visual feedback.
    No preview · Article · Dec 2015 · Journal of Neural Engineering
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    ABSTRACT: Objective: Surface electromyography (sEMG) is often used as a control signal in neuromuscular electrical stimulation (NMES) systems to enhance the voluntary control and proprioceptive sensory feedback of paralyzed patients. Most sEMG-controlled NMES systems use the envelope of the sEMG signal to modulate the stimulation intensity (current amplitude or pulse width) with a constant frequency. The aims of this study were to develop a strategy that co-modulates frequency and pulse width based on features of the sEMG signal and to investigate the torque-reproduction performance and the level of fatigue resistance achieved with our strategy. Approach: We examined the relationships between wrist torque and two stimulation parameters (frequency and pulse width) and between wrist torque and two sEMG time-domain features (mean absolute value (MAV) and number of slope sign changes (NSS)) in eight healthy volunteers. By using wrist torque as an intermediate variable, customized and generalized transfer functions were constructed to convert the two features of the sEMG signal into the two stimulation parameters, thereby establishing a MAV/NSS dual-coding (MNDC) algorithm. Wrist torque reproduction performance was assessed by comparing the torque generated by the algorithms with that originally recorded during voluntary contractions. Muscle fatigue was assessed by measuring the decline percentage of the peak torque and by comparing the torque time integral of the response to test stimulation trains before and after fatigue sessions. Main results: The MNDC approach could produce a wrist torque that closely matched the voluntary wrist torque. In addition, a smaller decay in the wrist torque was observed after the MNDC-coded fatigue stimulation was applied than after stimulation using pulse-width modulation alone. Significance: Compared with pulse-width modulation stimulation strategies that are based on sEMG detection, the MNDC strategy is more effective for both voluntary muscle force reproduction and muscle fatigue reduction.
    No preview · Article · Dec 2015 · Journal of Neural Engineering
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    ABSTRACT: Objective: Neurotechnology can contribute to the usability assessment of products by providing objective measures of neural workload and can uncover usability impediments that are not consciously perceived by test persons. In this study, the neural processing effort imposed on the viewer of 3D television by shutter glasses was quantified as a function of shutter frequency. In particular, we sought to determine the critical shutter frequency at which the 'neural flicker' vanishes, such that visual fatigue due to this additional neural effort can be prevented by increasing the frequency of the system. Approach: Twenty-three participants viewed an image through 3D shutter glasses, while multichannel electroencephalogram (EEG) was recorded. In total ten shutter frequencies were employed, selected individually for each participant to cover the range below, at and above the threshold of flicker perception. The source of the neural flicker correlate was extracted using independent component analysis and the flicker impact on the visual cortex was quantified by decoding the state of the shutter from the EEG. Main result: Effects of the shutter glasses were traced in the EEG up to around 67 Hz-about 20 Hz over the flicker perception threshold-and vanished at the subsequent frequency level of 77 Hz. Significance: The impact of the shutter glasses on the visual cortex can be detected by neurotechnology even when a flicker is not reported by the participants. Potential impact. Increasing the shutter frequency from the usual 50 Hz or 60 Hz to 77 Hz reduces the risk of visual fatigue and thus improves shutter-glass-based 3D usability.
    No preview · Article · Dec 2015 · Journal of Neural Engineering