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IEEE Transactions on Neural Systems and Rehabilitation Engineering

Fabrizio Leo, Elena Cocchi, Luca Brayda
Vision loss has severe impacts on physical, social and emotional well-being. The education of blind children poses issues as many scholar disciplines (e.g. geometry, mathematics) are normally taught by heavily relying on vision. Touch-based assistive technologies are potential tools to provide graphical contents to blind users, improving learning possibilities and social inclusion. Raised-lines drawings are still the golden standard, but stimuli cannot be reconfigured or adapted and the blind person constantly requires assistance...
October 20, 2016: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Diego L Guarin, Robert E Kearney
The mechanical properties of a joint are determined by the combination of intrinsic and reflex mechanisms. However, in some situations the reflex contributions are small so that intrinsic mechanisms play the dominant role in the control of posture and movement. The intrinsic mechanisms, characterized by the joint compliance, can be described well by a second order, linear model for small perturbations around an operating point defined by mean position and torque. However, the compliance parameters depend strongly on the operating point...
October 19, 2016: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Dongrae Cho, Beomjun Min, Jongin Kim, Boreom Lee
In this study, we examined the phase locking value (PLV) for seizure prediction, particularly, in the gamma frequency band. We prepared simulation data and 65 clinical cases of seizure. In addition, various filtering algorithms including bandpass filtering, empirical mode decomposition, multivariate empirical mode decomposition and noise-assisted multivariate empirical mode decomposition (NA-MEMD) were used to decompose spectral components from the data. Moreover, in the case of clinical data, the PLVs were used to classify between interictal and preictal stages using a support vector machine...
October 19, 2016: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Dong Qian, Bei Wang, Yun Qing, Tao Zhang, Yu Zhang, Xing Wang, Masatoshi Nakamura
Daytime short nap involves physiological processes, such as alertness, drowsiness and sleep. The study of the relationship between drowsiness and nap based on physiological signals is a great way to have a better understanding of the periodical rhymes of physiological states. A model of Bayesian nonnegative CP decomposition (BNCPD) was proposed to extract common multiway features from the group-level electroencephalogram (EEG) signals. As an extension of the nonnegative CP decomposition, the BNCPD model involves prior distributions of factor matrices, while the underlying CP rank could be determined automatically based on a Bayesian nonparametric approach...
October 19, 2016: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Shanhai Jin, Noriyasu Iwamoto, Kazunobu Hashimoto, Motoji Yamamoto
This paper presents a new soft wearable robotic suit for energy-efficient walking in daily activities for elderly persons. The presented robotic suit provides a small yet effective assistive force for hip flexion through winding belts that include elastic elements. In addition, it does not restrict the range of movement in the lower limbs. Moreover, its structure is simple and lightweight, and thus wearers can easily take the device on and off by themselves. Experimental results on nine elderly subjects (age = 74...
October 12, 2016: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Ross A Bogey, Lee A Barnes
The force of a single muscle is not directly measurable without invasive methods. Yet invasive techniques are not appropriate for clinical use, thus a non-invasive technique that combined the electromyographic (EMG) signal and a neuromuscular model was developed to determine in vivo active muscle forces at the hip. The EMG-to-force processing (EFP) model included active and passive moment components, and the net EFP moment was compared with the hip moment obtained with standard inverse dynamics techniques ("gold standard")...
October 12, 2016: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Fan Zhang, Peter Bohlen, Michael D Lewek, He Huang
This study investigated the feasibility of predicting intrinsically caused trips (ICTs) in individuals with stroke. Gait kinematics collected from twelve individuals with chronic stroke, who demonstrated ICTs in treadmill walking, were analyzed. A prediction algorithm based on the outlier principle was employed. Sequential forward selection (SFS) and minimum-redundancy- maximum-relevance (mRMR) were used separately to identify the precursors for accurate ICT prediction. The results showed that it was feasible to predict ICTs around 50- 260ms before ICTs occurred in the swing phase by monitoring lower limb kinematics during the preceding stance phase...
October 11, 2016: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Shahab Shahdoost, Randolph Nudo, Pedram Mohseni
Brain-machine-body interfaces (BMBIs) aim to create an artificial connection in the nervous system by converting neural activity recorded from one cortical region to electrical stimuli delivered to another cortical region, spinal cord, or muscles in real-time. In particular, conditioning-mode BMBIs utilize such activity-dependent stimulation strategies to induce functional re-organization in the nervous system and promote functional recovery after injury by exploiting mechanisms underlying neuroplasticity. This paper reports on reconfigurable, field-programmable gate array (FPGA)-based implementation of a translation algorithm to extract multichannel stimulus trigger signals from intracortical neural spike activity...
October 5, 2016: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Luzheng Bi, Yun Lu, Xinan Fan, Jinling Lian, Yili Liu
Directly using brain signals rather than limbs to steer a vehicle may not only help disabled people to control an assistive vehicle, but also provide a complementary means of control for a wider driving community. In this paper, to simulate and predict driver performance in steering a vehicle with brain signals, we propose a driver brain-controlled steering model by combining an extended queuing network-based driver model with a brain-computer interface (BCI) performance model. Experimental results suggest that the proposed driver brain-controlled steering model has performance close to that of real drivers with good performance in brain-controlled driving...
September 28, 2016: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Tao Zhang, Wanzhong Chen
Achieving the goal of detecting seizure activity automatically using electroencephalogram (EEG) signals is of great importance and significance for the treatment of epileptic seizures. To realize this aim, a newly-developed time-frequency analytical algorithm, namely local mean decomposition (LMD), is employed in the presented study. LMD is able to decompose an arbitrary signal into a series of product functions (PFs). Primarily, the raw EEG signal is decomposed into several PFs, and then the temporal statistical and non-linear features of the first five PFs are calculated...
September 20, 2016: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Rasmus Nielsen, Winnie Jensen
Ischemic stroke causes a series of complex pathophysiological events in the brain. Electrical stimulation of the brain has been considered as a novel neuroprotection intervention to save the penumbra. However, the effect on the cells' responsiveness and their ability to survive has yet to be established. The objective of the present study was to investigate the effects of low-frequency intracortical electrical stimulation (lf-ICES) applied to the ischemia-affected sensorimotor cortex immediately following ischemic stroke...
September 16, 2016: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Wafa Tigra, Benjamin Navarro, Andrea Cherubini, Xavier Gorron, Anthony Gelis, Charles Fattal, David Guiraud, Christine Azevedo-Coste
This article introduces a new human-machine interface for individuals with tetraplegia. We investigated the feasibility of piloting an assistive device by processing supra-lesional muscle responses online. The ability to voluntarily contract a set of selected muscles was assessed in five spinal cord-injured subjects through electromyographic (EMG) analysis. Two subjects were also asked to use the EMG interface to control palmar and lateral grasping of a robot hand. The use of different muscles and control modalities was also assessed...
September 14, 2016: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Shen Ren, Junhua Li, Fumihiko Taya, Joshua deSouza, Nitish Thakor, Anastasios Bezerianos
The analysis of the topology and organisation of brain networks is known to greatly benefit from network measures in graph theory. However, to evaluate dynamic changes of brain functional connectivity, more sophisticated quantitative metrics characterising temporal evolution of brain topological features are required. To simplify conversion of time-varying brain connectivity to a static graph representation is straightforward but the procedure loses temporal information that could be critical in understanding the brain functions...
September 9, 2016: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Reza Foodeh, Abed Khorasani, Vahid Shalchyan, Mohammad Reza Daliri
In this paper a novel automated and unsupervised method for removing artifacts from multichannel field potential signals is introduced which can be used in brain computer interface (BCI) applications. The method, which is called minimum noise estimate (MNE) filter is based on an iterative thresholding followed by Rayleigh quotient which tries to find an estimate of the noise and to minimize it over the original signal. MNE filter is capable to operate without any prior information about field potential signals...
September 7, 2016: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Reem Al-Halimi, Medhat Moussa
In this paper, we report on the results of a study that was conducted to examine how users suffering from severe upper-extremity disabilities can control a 6 Degrees of Freedom (DOF) robotics arm to complete complex activities of daily living. The focus of the study is not on assessing the robot arm but on examining the human-robot interaction patterns. Three participants were recruited. Each participant was asked to perform three tasks: eating three pieces of pre-cut bread from a plate, drinking three sips of soup from a bowl, and opening a right-handed door with lever handle...
August 26, 2016: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Jason R Franz, Carrie Francis, Matt Allen, Darryl G Thelen
Visuomotor entrainment, or the synchronization of motor responses to visual stimuli, is a naturally emergent phenomenon in human standing. Our purpose was to investigate the prevalence and resolution of visuomotor entrainment in walking and the frequency-dependent response of walking balance to perturbations. We used a virtual reality environment to manipulate optical flow in ten healthy young adults during treadmill walking. A motion capture system recorded trunk, sacrum, and heel marker trajectories during a series of 3-min conditions in which we perturbed a virtual hallway mediolaterally with systematic changes in the driving frequencies of perceived motion...
August 26, 2016: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Hong Yap, Nazir Kamaldin, Jeong Lim, Fatima Nasrallah, James Goh, Chen-Hua Yeow
In this paper, we present the design, fabrication and evaluation of a soft wearable robotic glove, which can be used with functional Magnetic Resonance imaging (fMRI) during the hand rehabilitation and task specific training. The soft wearable robotic glove, called MR-Glove, consists of two major components: a) a set of soft pneumatic actuators and b) a glove. The soft pneumatic actuators, which are made of silicone elastomers, generate bending motion and actuate finger joints upon pressurization. The device is MR-compatible as it contains no ferromagnetic materials and operates pneumatically...
August 25, 2016: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Reva E Johnson, Konrad P Koerding, Levi J Hargrove, Jonathon W Sensinger
In this paper we asked the question: if we artificially raise the variability of torque control signals to match that of EMG, do subjects make similar errors and have similar uncertainty about their movements? We answered this question using two experiments in which subjects used three different control signals: torque, torque+noise, and EMG. First, we measured error on a simple target-hitting task in which subjects received visual feedback only at the end of their movements. We found that even when the signal-to-noise ratio was equal across EMG and torque+noise control signals, EMG resulted in larger errors...
August 25, 2016: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Raphael Dumas, Rickard Branemark, Laurent Frossard
Quantitative assessments of prostheses performances rely more and more frequently on gait analysis focusing on prosthetic knee joint forces and moments computed by inverse dynamics. However, this method is prone to errors, as demonstrated in comparison with direct measurements of these forces and moments. The magnitude of errors reported in the literature seems to vary depending on prosthetic components. Therefore, the purposes of this study were (A) to quantify and compare the magnitude of errors in knee joint forces and moments obtained with inverse dynamics and direct measurements on ten participants with transfemoral amputation during walking and (B) to investigate if these errors can be characterised for different prosthetic knees...
August 18, 2016: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Na Lu, Tengfei Li, Xiaodong Ren, Hongyu Miao
Motor imagery classification is an important topic in brain computer interface (BCI) research that enables the recognition of a subject's intension to, e.g., implement prosthesis control. The brain dynamics of motor imagery are usually measured by electroencephalography (EEG) as nonstationary time series of low signal-to-noise ratio. Although a variety of methods have been previously developed to learn EEG signal features, the deep learning idea has rarely been explored to generate new representation of EEG features and achieve further performance improvement for motor imagery classification...
August 17, 2016: IEEE Transactions on Neural Systems and Rehabilitation Engineering
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