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: 3.42

Impact Factor Rankings

2015 Impact Factor Available summer 2015
2013 / 2014 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 4.05
Cited half-life 3.80
Immediacy index 0.67
Eigenfactor 0.01
Article influence 1.25
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
    ​ green

Publications in this journal

  • [Show abstract] [Hide abstract]
    ABSTRACT: In this paper, we propose a novel method to determine the circadian variation of scalp electroencephalogram (EEG) in both individual and group levels using a correlation sum measure, quantifying self-similarity of the EEG relative energy across waking epochs. We analysed EEG recordings from central-parietal and occipito-parietal montages in nine healthy subjects undergoing a 72 h ultradian sleep-wake cycle protocol. Each waking epoch (∼1 s) of every nap opportunity was decomposed using the wavelet packet transform, and the relative energy for that epoch was calculated in the desired frequency band using the corresponding wavelet coefficients. Then, the resulting set of energy values was resampled randomly to generate different subsets with equal number of elements. The correlation sum of each subset was then calculated over a range of distance thresholds, and the average over all subsets was computed. This average value was finally scaled for each nap opportunity and considered as a new circadian measure. According to the evaluation results, a clear circadian rhythm was identified in some EEG frequency ranges, particularly in 4-8 Hz and 10-12 Hz. The correlation sum measure not only was able to disclose the circadian rhythm on the group data but also revealed significant circadian variations in most individual cases, as opposed to previous studies only reporting the circadian rhythms on a population of subjects. Compared to a naive measure based on the EEG absolute energy in the frequency band of interest, the proposed measure showed a clear superiority using both individual and group data. Results also suggested that the acrophase (i.e., the peak) of the circadian rhythm in 10-12 Hz occurs close to the core body temperature minimum. These results confirm the potential usefulness of the proposed EEG-based measure as a non-invasive circadian marker.
    Journal of Neural Engineering 10/2015; 12(5). DOI:10.1088/1741-2560/12/5/056004
  • Quanying Liu · Joshua H Balsters · Marc Baechinger · Onno van der Groen · Nicole Wenderoth · Dante Mantini
    [Show abstract] [Hide abstract]
    ABSTRACT: In electroencephalography (EEG) measurements, the signal of each recording electrode is contrasted with a reference electrode or a combination of electrodes. The estimation of a neutral reference is a long-standing issue in EEG data analysis, which has motivated the proposal of different re-referencing methods, among which linked-mastoid re-referencing (LMR), average re-referencing (AR) and reference electrode standardization technique (REST). In this study we quantitatively assessed the extent to which the use of a high-density montage and a realistic head model can impact on the optimal estimation of a neutral reference for EEG recordings. Using simulated recordings generated by projecting specific source activity over the sensors, we assessed to what extent AR, REST and LMR may distort the scalp topography. We examined the impact electrode coverage has on AR and REST, and how accurate the REST reconstruction is for realistic and less realistic (three-layer and single-layer spherical) head models, and with possible uncertainty in the electrode positions. We assessed LMR, AR and REST also in the presence of typical EEG artifacts that are mixed in the recordings. Finally, we applied them to real EEG data collected in a target detection experiment to corroborate our findings on simulated data. Both AR and REST have relatively low reconstruction errors compared to LMR, and that REST is less sensitive than AR and LMR to artifacts mixed in the EEG data. For both AR and REST, high electrode density yields low re-referencing reconstruction errors. A realistic head model is critical for REST, leading to a more accurate estimate of a neutral reference compared to spherical head models. With a low-density montage, REST shows a more reliable reconstruction than AR either with a realistic or a three-layer spherical head model. Conversely, with a high-density montage AR yields better results unless precise information on electrode positions is available. Our study is the first to quantitatively assess the performance of EEG re-referencing techniques in relation to the use of a high-density montage and a realistic head model. We hope our study will help researchers in the choice of the most effective re-referencing approach for their EEG studies.
    Journal of Neural Engineering 08/2015; 12(5):056012. DOI:10.1088/1741-2560/12/5/056012
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    ABSTRACT: The dexterous manipulation of objects depends heavily on somatosensory signals from the limb. The development of anthropomorphic robotic arms and of algorithms to decode intended movements from neuronal signals has stimulated the need to restore somatosensation for use in upper-limb neuroprostheses. Without touch and proprioception, patients have difficulty controlling prosthetic limbs to a level that justifies the required invasive surgery. Intracortical microstimulation (ICMS) through chronically implanted electrode arrays has the potential to provide rich and intuitive sensory feedback. This approach to sensory restoration requires, however, that the evoked sensations remain stable over time. To investigate the stability of ICMS-evoked sensations, we measured the ability of non-human primates to detect ICMS over experimental sessions that spanned years. We found that the performance of the animals remained highly stable over time, even when they were tested with electrodes that had experienced extensive stimulation. Given the stability of the sensations that it evokes, ICMS may thus be a viable approach for sensory restoration.
    Journal of Neural Engineering 08/2015; 12(5):056010. DOI:10.1088/1741-2560/12/5/056010
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    ABSTRACT: We have developed an asynchronous brain-machine interface (BMI)-based lower limb exoskeleton control system based on steady-state visual evoked potentials (SSVEPs). By decoding electroencephalography signals in real-time, users are able to walk forward, turn right, turn left, sit, and stand while wearing the exoskeleton. SSVEP stimulation is implemented with a visual stimulation unit, consisting of five light emitting diodes fixed to the exoskeleton. A canonical correlation analysis (CCA) method for the extraction of frequency information associated with the SSVEP was used in combination with k-nearest neighbors. Overall, 11 healthy subjects participated in the experiment to evaluate performance. To achieve the best classification, CCA was first calibrated in an offline experiment. In the subsequent online experiment, our results exhibit accuracies of 91.3 ± 5.73%, a response time of 3.28 ± 1.82 s, an information transfer rate of 32.9 ± 9.13 bits/min, and a completion time of 1100 ± 154.92 s for the experimental parcour studied. The ability to achieve such high quality BMI control indicates that an SSVEP-based lower limb exoskeleton for gait assistance is becoming feasible.
    Journal of Neural Engineering 08/2015; 12(5):056009. DOI:10.1088/1741-2560/12/5/056009
  • [Show abstract] [Hide abstract]
    ABSTRACT: This work deals with studying and improving the insulation lifetime of polyimide-insulated thin film neural implants, or related polyimide-based medical implants. The evolution of the leak impedance of insulated conductors was investigated in saline water at 40 °C. The fabrication process as commonly found in literature for polyimide/platinum/polyimide microelectrode arrays was compared with three possible improvements: one based on lowering the curing temperature of the lower layer, one based on chemical activation and one based on an additional plasma activation step. The lower curing temperature process was found to yield a 7.5-fold improved lifetime compared with the state of the art process. Also, the leak impedances found after soak testing are an order of magnitude higher compared to the standard process. By improving the lifetime and insulation impedance of polyimide insulation with one order of magnitude, this work increases the applicability of polyimide in chronic thin film neural implants considerably.
    Journal of Neural Engineering 08/2015; 12(5):054001. DOI:10.1088/1741-2560/12/5/054001
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    ABSTRACT: People with disabilities may control devices such as a computer or a wheelchair by means of a brain-computer interface (BCI). BCI based on steady-state visual evoked potentials (SSVEP) requires visual stimulation of the user. However, this SSVEP-based BCI suffers from the 'Midas touch effect', i.e., the BCI can detect an SSVEP even when the user is not gazing at the stimulus. Then, these incorrect detections deteriorate the performance of the system, especially in asynchronous BCI because ongoing EEG is classified. In this paper, a novel transitory response of the attention-level of the user is reported. It was used to develop a hybrid BCI (hBCI). Three methods are proposed to detect the attention-level of the user. They are based on the alpha rhythm and theta/beta rate. The proposed hBCI scheme is presented along with these methods. Hence, the hBCI sends a command only when the user is at a high-level of attention, or in other words, when the user is really focused on the task being performed. The hBCI was tested over two different EEG datasets. The performance of the hybrid approach is superior to the standard one. Improvements of 20% in accuracy and 10 bits min(-1) are reported. Moreover, the attention-level is extracted from the same EEG channels used in SSVEP detection and this way, no extra hardware is needed. A transitory response of EEG signal is used to develop the attention-SSVEP hBCI which is capable of reducing the Midas touch effect.
    Journal of Neural Engineering 08/2015; 12(5):056007. DOI:10.1088/1741-2560/12/5/056007
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    ABSTRACT: Ketamine is a widely used drug with clinical and research applications, and also known to be used as a recreational drug. Ketamine produces conspicuous changes in the electrocorticographic (ECoG) signals observed both in humans and rodents. In rodents, the intracranial ECoG displays a high-frequency oscillation (HFO) which power is modulated nonlinearly by ketamine dose. Despite the widespread use of ketamine there is no model description of the relationship between the pharmacokinetic-pharmacodynamics (PK-PDs) of ketamine and the observed HFO power. In the present study, we developed a PK-PD model based on estimated ketamine concentration, its known pharmacological actions, and observed ECoG effects. The main pharmacological action of ketamine is antagonism of the NMDA receptor (NMDAR), which in rodents is accompanied by an HFO observed in the ECoG. At high doses, however, ketamine also acts at non-NMDAR sites, produces loss of consciousness, and the transient disappearance of the HFO. We propose a two-compartment PK model that represents the concentration of ketamine, and a PD model based in opposing effects of the NMDAR and non-NMDAR actions on the HFO power. We recorded ECoG from the cortex of rats after two doses of ketamine, and extracted the HFO power from the ECoG spectrograms. We fit the PK-PD model to the time course of the HFO power, and showed that the model reproduces the dose-dependent profile of the HFO power. The model provides good fits even in the presence of high variability in HFO power across animals. As expected, the model does not provide good fits to the HFO power after dosing the pure NMDAR antagonist MK-801. Our study provides a simple model to relate the observed electrophysiological effects of ketamine to its actions at the molecular level at different concentrations. This will improve the study of ketamine and rodent models of schizophrenia to better understand the wide and divergent range of effects that ketamine has.
    Journal of Neural Engineering 08/2015; 12(5):056006. DOI:10.1088/1741-2560/12/5/056006
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    ABSTRACT: Ojective. Axons are guided toward desired targets through a series of choice points that they navigate by sensing cues in the cellular environment. A better understanding of how microenvironmental factors influence neurite growth during development can inform strategies to address nerve injury. Therefore, there is a need for biomimetic models to systematically investigate the influence of guidance cues at such choice points. We ran an adapted in silico biased turning axon growth model under the influence of nerve growth factor (NGF) and compared the results to corresponding in vitro experiments. We examined if growth simulations were predictive of neurite population behavior at a choice point. We used a biphasic micropatterned hydrogel system consisting of an outer cell restrictive mold that enclosed a bifurcated cell permissive region and placed a well near a bifurcating end to allow proteins to diffuse and form a gradient. Experimental diffusion profiles in these constructs were used to validate a diffusion computational model that utilized experimentally measured diffusion coefficients in hydrogels. The computational diffusion model was then used to establish defined soluble gradients within the permissive region of the hydrogels and maintain the profiles in physiological ranges for an extended period of time. Computational diffusion profiles informed the neurite growth model, which was compared with neurite growth experiments in the bifurcating hydrogel constructs. Results indicated that when applied to the constrained choice point geometry, the biased turning model predicted experimental behavior closely. Results for both simulated and in vitro neurite growth studies showed a significant chemoattractive response toward the bifurcated end containing an NGF gradient compared to the control, though some neurites were found in the end with no NGF gradient. The integrated model of neurite growth we describe will allow comparison of experimental studies against growth cone guidance computational models applied to axon pathfinding at choice points.
    Journal of Neural Engineering 08/2015; 12(4). DOI:10.1088/1741-2560/12/4/046012
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    ABSTRACT: Wireless control and power harvesting systems that operate injectable, cellular-scale optoelectronic components provide important demonstrated capabilities in neuromodulatory techniques such as optogenetics. Here, we report a radio frequency (RF) control/harvesting device that offers dramatically reduced size, decreased weight and improved efficiency compared to previously reported technologies. Combined use of this platform with ultrathin, multijunction, high efficiency solar cells allows for hundred-fold reduction of transmitted RF power, which greatly enhances the wireless coverage. Fabrication involves separate construction of the harvester and the injectable µ-ILEDs. To test whether the presence of the implantable device alters behavior, we implanted one group of wild type mice and compared sociability behavior to unaltered controls. Social interaction experiments followed protocols defined by Silverman et al. with minor modifications. The results presented here demonstrate that miniaturized RF harvesters, and RF control strategies with photovoltaic harvesters can, when combined with injectable µ-ILEDs, offer versatile capabilities in optogenetics. Experimental and modeling studies establish a range of effective operating conditions for these two approaches. Optogenetics studies with social groups of mice demonstrate the utility of these systems. The addition of miniaturized, high performance photovoltaic cells significantly expands the operating range and reduces the required RF power. The platform can offer capabilities to modulate signaling path in the brain region of freely-behaving animals. These suggest its potential for widespread use in neuroscience.
    Journal of Neural Engineering 07/2015; 12(5):056002. DOI:10.1088/1741-2560/12/5/056002
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    ABSTRACT: Brain-computer interfaces (BCIs) represent a technology with the potential to rehabilitate a range of traumatic and degenerative nervous system conditions but require a time-consuming training process to calibrate. An area of BCI research known as transfer learning is aimed at accelerating training by recycling previously recorded training data across sessions or subjects. Training data, however, is typically transferred from one electrode configuration to another without taking individual head anatomy or electrode positioning into account, which may underutilize the recycled data. We explore transfer learning with the use of source imaging, which estimates neural activity in the cortex. Transferring estimates of cortical activity, in contrast to scalp recordings, provides a way to compensate for variability in electrode positioning and head morphologies across subjects and sessions. Based on simulated and measured electroencephalography activity, we trained a classifier using data transferred exclusively from other subjects and achieved accuracies that were comparable to or surpassed a benchmark classifier (representative of a real-world BCI). Our results indicate that classification improvements depend on the number of trials transferred and the cortical region of interest. These findings suggest that cortical source-based transfer learning is a principled method to transfer data that improves BCI classification performance and provides a path to reduce BCI calibration time.
    Journal of Neural Engineering 07/2015; 12(4):046027. DOI:10.1088/1741-2560/12/4/046027
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    ABSTRACT: Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation technique to modify neural excitability. Using multi-array tDCS, we investigate the influence of inter-individually varying head tissue conductivity profiles on optimal electrode configurations for an auditory cortex stimulation. In order to quantify the uncertainty of the optimal electrode configurations, multi-variate generalized polynomial chaos expansions of the model solutions are used based on uncertain conductivity profiles of the compartments skin, skull, gray matter, and white matter. Stochastic measures, probability density functions, and sensitivity of the quantities of interest are investigated for each electrode and the current density at the target with the resulting stimulation protocols visualized on the head surface. We demonstrate that the optimized stimulation protocols are only comprised of a few active electrodes, with tolerable deviations in the stimulation amplitude of the anode. However, large deviations in the order of the uncertainty in the conductivity profiles could be noted in the stimulation protocol of the compensating cathodes. Regarding these main stimulation electrodes, the stimulation protocol was most sensitive to uncertainty in skull conductivity. Finally, the probability that the current density amplitude in the auditory cortex target region is supra-threshold was below 50%. The results suggest that an uncertain conductivity profile in computational models of tDCS can have a substantial influence on the prediction of optimal stimulation protocols for stimulation of the auditory cortex. The investigations carried out in this study present a possibility to predict the probability of providing a therapeutic effect with an optimized electrode system for future auditory clinical and experimental procedures of tDCS applications.
    Journal of Neural Engineering 07/2015; 12(4):046028. DOI:10.1088/1741-2560/12/4/046028