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

2015 Impact Factor Available summer 2016
2014 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: A brain-computer interface (BCI) is an interface that uses signals from the brain to control a computer. BCIs will likely become important tools for severely paralyzed patients to restore interaction with the environment. The sensorimotor cortex is a promising target brain region for a BCI due to the detailed topography and minimal functional interference with other important brain processes. Previous studies have shown that attempted movements in paralyzed people generate neural activity that strongly resembles actual movements. Hence decodability for BCI applications can be studied in able-bodied volunteers with actual movements. Approach: In this study we tested whether mouth movements provide adequate signals in the sensorimotor cortex for a BCI. The study was executed using fMRI at 7 T to ensure relevance for BCI with cortical electrodes, as 7 T measurements have been shown to correlate well with electrocortical measurements. Twelve healthy volunteers executed four mouth movements (lip protrusion, tongue movement, teeth clenching, and the production of a larynx activating sound) while in the scanner. Subjects performed a training and a test run. Single trials were classified based on the Pearson correlation values between the activation patterns per trial type in the training run and single trials in the test run in a 'winner-takes-all' design. Main results: Single trial mouth movements could be classified with 90% accuracy. The classification was based on an area with a volume of about 0.5 cc, located on the sensorimotor cortex. If voxels were limited to the surface, which is accessible for electrode grids, classification accuracy was still very high (82%). Voxels located on the precentral cortex performed better (87%) than the postcentral cortex (72%). Significance: The high reliability of decoding mouth movements suggests that attempted mouth movements are a promising candidate for BCI in paralyzed people.
    Journal of Neural Engineering 11/2015; 12(6):066026. DOI:10.1088/1741-2560/12/6/066026
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    ABSTRACT: Objective: Simplified neuronal circuits are required for investigating information representation in nervous systems and for validating theoretical neural network models. Here, we developed patterned neuronal circuits using micro fabricated devices, comprising a micro-well array bonded to a microelectrode-array substrate. Approach: The micro-well array consisted of micrometre-scale wells connected by tunnels, all contained within a silicone slab called a micro-chamber. The design of the micro-chamber confined somata to the wells and allowed axons to grow through the tunnels bidirectionally but with a designed, unidirectional bias. We guided axons into the point of the arrow structure where one of the two tunnel entrances is located, making that the preferred direction. Main results: When rat cortical neurons were cultured in the wells, their axons grew through the tunnels and connected to neurons in adjoining wells. Unidirectional burst transfers and other asymmetric signal-propagation phenomena were observed via the substrate-embedded electrodes. Seventy-nine percent of burst transfers were in the forward direction. We also observed rapid propagation of activity from sites of local electrical stimulation, and significant effects of inhibitory synapse blockade on bursting activity. Significance: These results suggest that this simple, substrate-controlled neuronal circuit can be applied to develop in vitro models of the function of cortical microcircuits or deep neural networks, better to elucidate the laws governing the dynamics of neuronal networks.
    Journal of Neural Engineering 11/2015; 12(6):066023. DOI:10.1088/1741-2560/12/6/066023
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    ABSTRACT: Objective: Oscillations are an important aspect of brain activity, but they often have a low signal-to-noise ratio (SNR) due to source-to-electrode mixing with competing brain activity and noise. Filtering can improve the SNR of narrowband signals, but it introduces ringing effects that may masquerade as genuine oscillations, leading to uncertainty as to the true oscillatory nature of the phenomena. Likewise, time-frequency analysis kernels have a temporal extent that blurs the time course of narrowband activity, introducing uncertainty as to timing and causal relations between events and/or frequency bands. Approach: Here, we propose a methodology that reveals narrowband activity within multichannel data such as electroencephalography, magnetoencephalography, electrocorticography or local field potential. The method exploits the between-channel correlation structure of the data to suppress competing sources by joint diagonalization of the covariance matrices of narrowband filtered and unfiltered data. Main results: Applied to synthetic and real data, the method effectively extracts narrowband components at unfavorable SNR. Significance: Oscillatory components of brain activity, including weak sources that are hard or impossible to observe using standard methods, can be detected and their time course plotted accurately. The method avoids the temporal artifacts of standard filtering and time-frequency analysis methods with which it remains complementary.
    Journal of Neural Engineering 10/2015; 12(6):066020. DOI:10.1088/1741-2560/12/6/066020
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    ABSTRACT: Objective: One approach to conveying sensory feedback in neuroprostheses is to electrically stimulate sensory neurons in the cortex. For this approach to be viable, it is critical that intracortical microstimulation (ICMS) causes minimal damage to the brain. Here, we investigate the effects of chronic ICMS on the neuronal tissue across a variety of stimulation regimes in non-human primates. We also examine each animal's ability to use their hand-the cortical representation of which is targeted by the ICMS-as a further assay of possible neuronal damage. Approach: We implanted electrode arrays in the primary somatosensory cortex of three Rhesus macaques and delivered ICMS four hours per day, five days per week, for six months. Multiple regimes of ICMS were delivered to investigate the effects of stimulation parameters on the tissue and behavior. Parameters included current amplitude (10-100 μA), pulse train duration (1, 5 s), and duty cycle (1/1, 1/3). We then performed a range of histopathological assays on tissue near the tips of both stimulated and unstimulated electrodes to assess the effects of chronic ICMS on the tissue and their dependence on stimulation parameters. Main results: While the implantation and residence of the arrays in the cortical tissue did cause significant damage, chronic ICMS had no detectable additional effect; furthermore, the animals exhibited no impairments in fine motor control. Significance: Chronic ICMS may be a viable means to convey sensory feedback in neuroprostheses as it does not cause significant damage to the stimulated tissue.
    Journal of Neural Engineering 10/2015; 12(6):066018. DOI:10.1088/1741-2560/12/6/066018
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    ABSTRACT: Objective: Representation of movement in the motor cortex (M1) has been widely studied in brain-machine interfaces (BMIs). The electromyogram (EMG) has greater bandwidth than the conventional kinematic variables (such as position, velocity), and is functionally related to the discharge of cortical neurons. As the stochastic information of EMG is derived from the explicit spike time structure, point process (PP) methods will be a good solution for decoding EMG directly from neural spike trains. Previous studies usually assume linear or exponential tuning curves between neural firing and EMG, which may not be true. Approach: In our analysis, we estimate the tuning curves in a data-driven way and find both the traditional functional-excitatory and functional-inhibitory neurons, which are widely found across a rat's motor cortex. To accurately decode EMG envelopes from M1 neural spike trains, the Monte Carlo point process (MCPP) method is implemented based on such nonlinear tuning properties. Main results: Better reconstruction of EMG signals is shown on baseline and extreme high peaks, as our method can better preserve the nonlinearity of the neural tuning during decoding. The MCPP improves the prediction accuracy (the normalized mean squared error) 57% and 66% on average compared with the adaptive point process filter using linear and exponential tuning curves respectively, for all 112 data segments across six rats. Compared to a Wiener filter using spike rates with an optimal window size of 50 ms, MCPP decoding EMG from a point process improves the normalized mean square error (NMSE) by 59% on average. Significance: These results suggest that neural tuning is constantly changing during task execution and therefore, the use of spike timing methodologies and estimation of appropriate tuning curves needs to be undertaken for better EMG decoding in motor BMIs.
    Journal of Neural Engineering 10/2015; 12(6):066014. DOI:10.1088/1741-2560/12/6/066014
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    ABSTRACT: Objective: Using the Medtronic Activa® PC + S system, this study investigated how passive joint manipulation, reaching behavior, and deep brain stimulation (DBS) modulate local field potential (LFP) activity in the subthalamic nucleus (STN) and globus pallidus (GP). Approach: Five non-human primates were implanted unilaterally with one or more DBS leads. LFPs were collected in montage recordings during resting state conditions and during motor tasks that facilitate the expression of parkinsonian motor signs. These recordings were made in the naïve state in one subject, in the parkinsonian state in two subjects, and in both naïve and parkinsonian states in two subjects. Main results: LFPs measured at rest were consistent over time for a given recording location and parkinsonian state in a given subject; however, LFPs were highly variable between subjects, between and within recording locations, and across parkinsonian states. LFPs in both naïve and parkinsonian states across all recorded nuclei contained a spectral peak in the beta band (10-30 Hz). Moreover, the spectral content of recorded LFPs was modulated by passive and active movement of the subjects' limbs. LFPs recorded during a cued-reaching task displayed task-related beta desynchronization in STN and GP. The bidirectional capabilities of the Activa® PC + S also allowed for recording LFPs while delivering DBS. The therapeutic effect of STN DBS on parkinsonian rigidity outlasted stimulation for 30-60 s, but there was no correlation with beta band power. Significance: This study emphasizes (1) the variability in spontaneous LFPs amongst subjects and (2) the value of using the Activa® PC + S system to record neural data in the context of behavioral tasks that allow one to evaluate a subject's symptomatology.
    Journal of Neural Engineering 10/2015; 12(6):066012. DOI:10.1088/1741-2560/12/6/066012
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    ABSTRACT: Objective: Deep brain stimulation (DBS) has become the standard treatment for advanced stages of Parkinson's disease (PD) and other motor disorders. Although the surgical procedure has improved in accuracy over the years thanks to imaging and microelectrode recordings, the underlying principles that render DBS effective are still debated today. The aim of this paper is to present initial findings around a new biomarker that is capable of assessing the efficacy of DBS treatment for PD which could be used both as a research tool, as well as in the context of a closed-loop stimulator. Approach: We have used a novel multi-channel stimulator and recording device capable of measuring the response of nervous tissue to stimulation very close to the stimulus site with minimal latency, rejecting most of the stimulus artefact usually found with commercial devices. We have recorded and analyzed the responses obtained intraoperatively in two patients undergoing DBS surgery in the subthalamic nucleus (STN) for advanced PD. Main results: We have identified a biomarker in the responses of the STN to DBS. The responses can be analyzed in two parts, an initial evoked compound action potential arising directly after the stimulus onset, and late responses (LRs), taking the form of positive peaks, that follow the initial response. We have observed a morphological change in the LRs coinciding with a decrease in the rigidity of the patients. Significance: These initial results could lead to a better characterization of the DBS therapy, and the design of adaptive DBS algorithms that could significantly improve existing therapies and help us gain insights into the functioning of the basal ganglia and DBS.
    Journal of Neural Engineering 10/2015; 12(6):066013. DOI:10.1088/1741-2560/12/6/066013
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    ABSTRACT: Objective: We consider the problem of optimal probing to learn connections in an evoked dynamic network. Such a network, in which each edge measures an input-output relationship between sites in sensor/actuator-space, is relevant to emerging applications in neural mapping and neural connectivity estimation. Approach: We show that the problem of scheduling nodes to a probe (i.e., stimulate) amounts to a problem of optimal sensor scheduling. Main results: By formulating the evoked network in state-space, we show that the solution to the greedy probing strategy has a convenient form and, under certain conditions, is optimal over a finite horizon. We adopt an expectation maximization technique to update the state-space parameters in an online fashion and demonstrate the efficacy of the overall approach in a series of detailed numerical examples. Significance: The proposed method provides a principled means to actively probe time-varying connections in neuronal networks. The overall method can be implemented in real time and is particularly well-suited to applications in stimulation-based cortical mapping in which the underlying network dynamics are changing over time.
    Journal of Neural Engineering 10/2015; 12(6):066011. DOI:10.1088/1741-2560/12/6/066011
  • [Show abstract] [Hide abstract]
    ABSTRACT: Objective: The majority of near-infrared spectroscopy (NIRS) brain-computer interface (BCI) studies have investigated binary classification problems. Limited work has considered differentiation of more than two mental states, or multi-class differentiation of higher-level cognitive tasks using measurements outside of the anterior prefrontal cortex. Improvements in accuracies are needed to deliver effective communication with a multi-class NIRS system. We investigated the feasibility of a ternary NIRS-BCI that supports mental states corresponding to verbal fluency task (VFT) performance, Stroop task performance, and unconstrained rest using prefrontal and parietal measurements. Approach: Prefrontal and parietal NIRS signals were acquired from 11 able-bodied adults during rest and performance of the VFT or Stroop task. Classification was performed offline using bagging with a linear discriminant base classifier trained on a 10 dimensional feature set. Main results: VFT, Stroop task and rest were classified at an average accuracy of 71.7% ± 7.9%. The ternary classification system provided a statistically significant improvement in information transfer rate relative to a binary system controlled by either mental task (0.87 ± 0.35 bits/min versus 0.73 ± 0.24 bits/min). Significance: These results suggest that effective communication can be achieved with a ternary NIRS-BCI that supports VFT, Stroop task and rest via measurements from the frontal and parietal cortices. Further development of such a system is warranted. Accurate ternary classification can enhance communication rates offered by NIRS-BCIs, improving the practicality of this technology.
    Journal of Neural Engineering 10/2015; 12(6):066008. DOI:10.1088/1741-2560/12/6/066008