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PubMed Journal Database | Journal of neural engineering - Page: 3 RSS

09:12 EDT 18th July 2019 | BioPortfolio

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Showing PubMed Articles 51–75 of 187 from Journal of neural engineering

Detection of brain stimuli using Ramanujan periodicity transforms.

The ability to efficiently match the frequency of the brain's response to repetitive visual stimuli in real time is the basis for reliable SSVEP-based Brain-Computer-Interfacing (BCI).

Three dimensional innervation zone imaging in spastic muscles of stroke survivors.

Objective-Outcome of botulinum toxin (BTX) therapy of post-stroke spasticity relies largely on accuracy of BTX injection to the proximity of innervation zones (IZs). Recently developed three-dimensional IZ imaging (3DIZI) is the only technique currently available to provide 3D distributions of IZs in vivo, yet its performance has not been validated under pathological conditions. Approach-The performance of 3DIZI was evaluated in the spastic biceps brachii muscles of four chronic stroke subjects. High-...

Developing a personalized closed-loop controller of medically-induced coma in a rodent model.

Personalized automatic control of medically-induced coma, a critical multi-day therapy in the intensive care unit, could greatly benefit clinical care and further provide a novel scientific tool for investigating how the brain response to anesthetic infusion rate changes during therapy. Personalized control would require real-time tracking of inter- and intra-subject variabilities in the brain response to anesthetic infusion rate while simultaneously delivering the therapy, which has not been achieved. Curr...

A probabilistic recurrent neural network for decoding hind limb kinematics from multi-segment recordings of the dorsal horn neurons.

Providing accurate and robust estimates of limb kinematics from recorded neural activities is prominent in closed-loop control of functional electrical stimulation (FES). A major issue in providing accurate decoding the limb kinematics is the decoding model. The primary goal of this study is to develop a decoding approach to model the dynamic interactions of neural systems for accurate decoding. Another critical issue is to find reliable recording sites. Up to now, neural recordings from spinal neural activ...

Regression convolutional neural network for improved simultaneous EMG control.

Deep learning models can learn representations of data that extract useful information in order to perform prediction without feature engineering. In this paper, an electromyography (EMG) control scheme with a regression convolutional neural network (CNN) is proposed as a substitute of conventional regression models that use purposefully designed features.

EEG representation using multi-instance framework on the manifold of symmetric positive definite matrices.

Abstract-The generalization and robustness of an electroencephalogram (EEG)-based system are crucial requirements in actual practices. To reach these goals, we propose a new EEG representation that provides a more realistic view of brain functionality by applying multi-instance (MI) framework to consider the non-stationarity of the EEG signal. In this representation, the non-stationarity of EEG is considered by describing the signal as a bag of relevant and irrelevant concepts. The concepts are provided by ...

EEG decoding of the target speaker in a cocktail party scenario: considerations regarding dynamic switching of talker location.

It has been shown that attentional selection in a simple dichotic listening paradigm can be decoded offline by reconstructing the stimulus envelope from single-trial neural response data. Here, we test the efficacy of this approach in an environment with non-stationary talkers. We then look beyond the envelope reconstructions themselves and consider whether incorporating the decoder values - which reflect the weightings applied to the multichannel EEG data at different time lags and scalp locations when rec...

Offline and online myoelectric pattern recognition analysis and real-time control of a robotic hand after spinal cord injury.

The objective of this study was to investigate the feasibility of applying myoelectric pattern recognition for controlling a robotic hand in individuals with spinal cord injury (SCI). Approach: Surface electromyogram (sEMG) signals of six hand motion patterns were recorded from 12 subjects with SCI. Online and offline classification performance of two classifiers (Gaussian Naive Bayes classifier, GNB, and Support Vector Machine, SVM) were investigated. An exoskeleton hand was then controlled in real-ti...

Speech synthesis from ECoG using densely connected 3D convolutional neural networks.

Direct synthesis of speech from neural signals could provide a fast and natural way of communication to people with neurological diseases. Invasively-measured brain activity (electrocorticography; ECoG) supplies the necessary temporal and spatial resolution to decode fast and complex processes such as speech production. A number of impressive advances in speech decoding using neural signals have been achieved in recent years, but the complex dynamics are still not fully understood. However, it is unlikely t...

Reliability of motor and sensory neural decoding by threshold crossings for intracortical brain-machine interface.

For intracortical neurophysiological studies, spike sorting is an important procedure to isolate single units for analyzing specific functions. However, whether spike sorting is necessary or not for neural decoding applications is controversial. Several studies showed that using threshold crossings (TC) instead of spike sorting could also achieve a similar satisfactory performance. However, such studies were limited in similar behavioral tasks, and the neural signal source mainly focused on the motor-relate...

Focal activation of neuronal circuits induced by microstimulation in the visual cortex.

Microstimulation to the cortical tissue applied with penetrating electrodes delivers current that spreads concentrically around the electrode tip and is known to evoke focal visual sensations, i.e., phosphenes. However, to date, there is no direct evidence depicting the spatiotemporal properties of neuronal activity induced immediately after microstimulation and how such activity drives the subsequent local cortical circuits. Approach: In the present study, we imaged the spatiotemporal distribution of...

Scoring upper-extremity motor function from EEG with artificial neural networks: a preliminary study.

Motor function of chronic stroke survivors is generally accessed using clinical motor assessments. These motor assessments are partially subjective and require prior training for the examiners. Additionally, those motor function assessments require the health professionals to be present in person. The method proposed in this paper has the potential to radically change the way motor function is assessed.

EEG-fTCD Hybrid brain-computer interface using template matching and wavelet decomposition.

We aim at developing a hybrid brain-computer interface that utilizes electroencephalography (EEG) and functional transcranial Doppler (fTCD). In this hybrid BCI, EEG and fTCD are used simultaneously to measure electrical brain activity and cerebral blood velocity respectively in response to flickering mental rotation and word generation tasks. In this paper, we improve both the accuracy and information transfer rate (ITR) of this novel hybrid brain computer interface (BCI) we designed in our previous work.

Improved in vitro electrophysiology using 3D-structured microelectrode arrays with a micro-mushrooms islets architecture capable of promoting topotaxis.

Planar microelectrode arrays are widely used in neuroscience but have relatively low electrical coupling and signal-to-noise ratio (SNR) in electrophysiology recordings. Strong efforts are therefore being made in improving microelectrode arrays (MEAs) performance, exploring both the microelectrode's shape and the array's architecture. Topographical features can be used in MEAs for promoting neuron-microelectrode contact, making 3D-microstructured MEAs an interesting design strategy for better electrophysiol...

Low-intensity ultrasound suppresses low-Mg2+-induced epileptiform discharges in juvenile mouse hippocampal slices.

It has been shown that low-intensity ultrasound (LIUS) can suppress seizures in some laboratory studies. However, the mechanism of the suppression effect of LIUS remains unclear. The goal of this study is to investigate the modulation effects of focused LIUS on epileptiform discharges in mouse hippocampal slices as well as the underlying mechanism. Approach. Epileptiform discharges in hippocampal slices of 8-day-old mice were induced by low-Mg<sup>2+</sup> artificial cerebrospinal fluid an...

Group-level and functional-region analysis of electric-field shape during cerebellar transcranial direct current stimulation with different electrode montages.

Cerebellar transcranial direct current stimulation (ctDCS) is a neuromodulation scheme that delivers a small current to the cerebellum. In this work, we computationally investigate the distributions and strength of the stimulation dosage during ctDCS with the aim of determining the targeted cerebellar regions of a group of subjects with different electrode montages. Approach. We used a new inter-individual registration method that permitted the projection of computed electric fields (EFs) from individu...

Deep learning for Electroencephalogram (EEG) classification tasks: A review.

Objective. Electroencephalography (EEG) analysis has been an important tool in neuroscience with applications in neuroscience, neural engineering (e.g., Brain-Computer Interfaces, BCI's), and even commercial applications. Many of the analytical tools used in EEG studies have used machine learning to uncover relevant information for neural classification and neuroimaging. Recently, the availability of large EEG data sets and advances in machine learning have both led to the deployment of deep learning ...

System based on subject-specific bands to recognize pedaling motor imagery: Towards a BCI for lower-limb rehabilitation.

The aim of this study is to propose a recognition system of pedaling motor imagery for lower-limb rehabilitation, which uses unsupervised methods to improve the feature extraction, and consequently the class discrimination of EEG patterns. Approach: After applying a spectrogram based on short-time Fourier transform (SSTFT), both sparseness constraints and total power are used on the time-frequency representation to automatically locate the subject-specific bands that pack the highest p...

Low-intensity pulsed ultrasound modulates multi-frequency band phase synchronization between LFPs and EMG in mice.

Low-intensity pulsed ultrasound stimulation (LIPUS) targeted to the mouse motor cortex can simultaneously induce local field potentials (LFPs) and electromyogram (EMG) responses. However, the functional coupling relationship between LFP and EMG signals has not been elucidated to date. This study aimed to investigate the phase synchronization between LFP and EMG signals induced by LIPUS over the mouse motor cortex. Approach. LIPUS at 500 kHz with varied sonication intensities and duty cycles (DCs), was...

EEG can predict speech intelligibility.

Speech signals have a remarkable ability to entrain brain activity to the rapid fluctuations of speech sounds. For instance, one can readily measure a correlation of the sound amplitude with the evoked responses of the electroencephalogram (EEG), and the strength of this correlation is indicative of whether the listener is attending to the speech. In this study we asked whether this stimulus-response correlation is also predictive of speech intelligibility. We hypothesized that when a listener fails to unde...

Cortical recruitment and functional dynamics in postural control adaptation and habituation during vibratory proprioceptive stimulation.

Maintaining upright posture is a complex task governed by the integration of afferent sensorimotor and visual information with compensatory neuromuscular reactions. The objective of the present work was to characterize the visual dependency and functional dynamics of cortical activation during postural control. Approach. Proprioceptic vibratory stimulation of calf muscles at 85 Hz was performed to evoke postural perturbation in open-eye (OE) and closed-eye (CE) experimental trials, with pseudorandom b...

Decoding neural activity to predict rat locomotion using intracortical and epidural arrays.

Recovery of voluntary gait after spinal cord injury (SCI) requires the restoration of effective motor cortical commands, either by means of a mechanical connection to the limbs, or by restored functional connections to muscles. The latter approach might use functional electrical stimulation (FES), driven by cortical activity, to restore voluntary movements. Moreover, there is evidence that this peripheral stimulation, synchronized with patients' voluntary effort, can strengthen descending projections and re...

Parallel, minimally-invasive implantation of ultra-flexible neural electrode arrays.

Implanted microelectrodes provide a unique means to directly interface with the nervous system, but have been limited by the lack of stable functionality. There is growing evidence suggesting that substantially reducing the mechanical rigidity of neural electrodes promotes tissue compatibility and improves their recording stability in both short- and long-terms. However, the miniaturized dimensions and ultraflexibility desired for mitigating tissue responses preclude the probe's self-supported penetration i...

Intraneural sensory feedback restores grip force control and motor coordination while using a prosthetic hand.

Tactile afferents in the human hand provide fundamental information about hand-environment interactions, which is used by the brain to adapt the motor output to the physical properties of the object being manipulated. A hand amputation disrupts both afferent and efferent pathways from/to the hand, completely invalidating the individual's motor repertoire. Although motor functions may be partially recovered by using a myoelectric prosthesis, providing functionally effective sensory feedback to users of prost...

Dynamics of motor cortical activity during naturalistic feeding behavior.

The orofacial primary motor cortex (MIo) plays a critical role in controlling tongue and jaw movements during oral motor functions, such as chewing, swallowing and speech. However, the neural mechanisms of MIo during naturalistic feeding are still poorly understood. There is a strong need for a systematic study of motor cortical dynamics during feeding behavior.


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