A Complex Stiffness Human Impedance Model With Customizable Exoskeleton Control

alkiviadis.1961 shared this article with you from Inoreader A Complex Stiffness Human Impedance Model With Customizable Exoskeleton Control Via Neural Systems and Rehabilitation Engineering, IEEE Transactions on - new TOC The natural impedance, or dynamic relationship between force and motion, of a human operator can determine the stability of exoskeletons that use interaction-torque feedback to amplify human strength. While human impedance is typically modelled as a linear system, our experiments on a single-joint exoskeleton testbed involving 10 human subjects show evidence of nonlinear behavior: a low-frequency asymptotic phase for the dynamic stiffness of the human that is different than the expected zero, and an unexpectedly consistent damping ratio as the stiffness and inertia vary. To explain these observations, this article considers a new frequency-domain model of the human joint dynamics featuring complex value stiffness comprising a real stiffness term and a hysteretic damping term. Using a statistical F-test we show that the hysteretic damping term is not only significant but is even more significant than the linear damping term. Further analysis reveals a linear trend linking hyster etic damping and the real part of the stiffness, which allows us to simplify the complex stiffness model down to a 1-parameter system. Then, we introduce and demonstrate a customizable fractional-order controller that exploits this hysteretic damping behavior to improve strength amplification bandwidth while maintaining stability, and explore a tuning approach which ensures that this stability property is robust to muscle co-contraction for each individual. View on the web Inoreader. Take back control of your news feed. Follow us on Twitter and Facebook.

A Computerized Method for Automatic Detection of Schizophrenia Using EEG Signals

alkiviadis.1961 shared this article with you from Inoreader A Computerized Method for Automatic Detection of Schizophrenia Using EEG Signals Via Neural Systems and Rehabilitation Engineering, IEEE Transactions on - new TOC Diagnosis of schizophrenia (SZ) is traditionally performed through patient's interviews by a skilled psychiatrist. This process is time-consuming, burdensome, subject to error and bias. Hence the aim of this study is to develop an automatic SZ identification scheme using electroencephalogram (EEG) signals that can eradicate the aforementioned problems and support clinicians and researchers. This study introduces a methodology design involving empirical mode decomposition (EMD) technique for diagnosis of SZ from EEG signals to perfectly handle the behavior of non-stationary and nonlinear EEG signals. In this study, each EEG signal is decomposed into intrinsic mode functions (IMFs) by the EMD algorithm and then twenty-two statistical characteristics/features are calculated from these IMFs. Among them, five features are selected as significant feature applying Kruskal Wallis test. The performance of the obtained feature set is tested through several renowned classifierson a SZ E EG dataset. Among the considered classifiers, theensemble bagged tree performed as the best classifier producing 93.21% correct classification rate for SZ, with an overall accuracy of 89.59% for IMF 2. These results indicate that EEG signals discriminate SZ patients from healthy control (HC) subjects efficiently and have the potential to become a tool for the psychiatrist to support the positive diagnosis of SZ. View on the web Inoreader. Take back control of your news feed. Follow us on Twitter and Facebook.

EEG-Based Prediction of Successful Memory Formation During Vocabulary Learning

alkiviadis.1961 shared this article with you from Inoreader EEG-Based Prediction of Successful Memory Formation During Vocabulary Learning Via Neural Systems and Rehabilitation Engineering, IEEE Transactions on - new TOC Previous Electroencephalography (EEG) and neuroimaging studies have found differences between brain signals for subsequently remembered and forgotten items during learning of items - it has even been shown that single trial prediction of memorization success is possible with a few target items. There has been little attempt, however, in validating the findings in an application-oriented context involving longer test spans with realistic learning materials encompassing more items. Hence, the present study investigates subsequent memory prediction within the application context of foreign-vocabulary learning. We employed an off-line, EEG-based paradigm in which Korean participants without prior German language experience learned 900 German words in paired-associate form. Our results using convolutional neural networks optimized for EEG-signal analysis show that above-chance classification is possible in this context allowing us to predict during learning which of the words would be successfully remembered later. View on the web Inoreader. Take back control of your news feed. Follow us on Twitter and Facebook.

Architectural Changes in Superficial and Deep Compartments of the Tibialis Anterior During Electrical Stimulation Over Different Sites

alkiviadis.1961 shared this article with you from Inoreader Architectural Changes in Superficial and Deep Compartments of the Tibialis Anterior During Electrical Stimulation Over Different Sites Via Neural Systems and Rehabilitation Engineering, IEEE Transactions on - new TOC Electrical stimulation is widely used in rehabilitation to prevent muscle weakness and to assist the functional recovery of neural deficits. Its application is however limited by the rapid development of muscle fatigue due to the non-physiological motor unit (MU) recruitment. This issue can be mitigated by interleaving muscle belly (mStim) and nerve stimulation (nStim) to distribute the temporal recruitment among different MU groups. To be effective, this approach requires the two stimulation modalities to activate minimally-overlapped groups of MUs. In this manuscript, we investigated spatial differences between mStim and nStim MU recruitment through the study of architectural changes of superficial and deep compartments of tibialis anterior (TA). We used ultrasound imaging to measure variations in muscle thickness, pennation angle, and fiber length during mStim, nStim, and voluntary (Vol) contractions at 15% and 25% of the maximal force. For both contraction levels, architect ural changes induced by nStim in the deep and superficial compartments were similar to those observed during Vol. Instead, during mStim superficial fascicles underwent a greater change compared to those observed during nStim and Vol, both in absolute magnitude and in their relative differences between compartments. These observations suggest that nStim results in a distributed MU recruitment over the entire muscle volume, similarly to Vol, whereas mStim preferentially activates the superficial muscle layer. The diversity between spatial recruitment of nStim and mStim suggests the involvement of different MU populations, which justifies strategies based on interleaved nerve/muscle stimulation to reduce muscle fatigue during electrically-induced contractions of TA. View on the web Inoreader. Take back control of your news feed. Follow us on Twitter and Facebook.

A Unified Analytical Framework With Multiple fNIRS Features for Mental Workload Assessment in the Prefrontal Cortex

alkiviadis.1961 shared this article with you from Inoreader A Unified Analytical Framework With Multiple fNIRS Features for Mental Workload Assessment in the Prefrontal Cortex Via Neural Systems and Rehabilitation Engineering, IEEE Transactions on - new TOC Knowing the actual level of mental workload is important to ensure the efficacy of brain-computer interface (BCI) based cognitive training. Extracting signals from limited area of a brain region might not reveal the actual information. In this study, a functional near-infrared spectroscopy (fNIRS) device equipped with multi-channel and multi-distance measurement capability was employed for the development of an analytical framework to assess mental workload in the prefrontal cortex (PFC). In addition to the conventional features, e.g. hemodynamic slope, we introduced a new feature – deep contribution ratio which is the proportion of cerebral hemodynamics to the fNIRS signals. Multiple sets of features were examined by a simple logical operator to suppress the false detection rate in identifying the activated channels. Using the number of activated channels as input to a linear support vector machine (SVM), the performance of the proposed analytical framework was assessed in c lassifying three levels of mental workload. The best set of features involves the combination of hemodynamic slope and deep contribution ratio, where the identified number of activated channels returned an average accuracy of 80.6% in predicting mental workload, compared to a single conventional feature (accuracy: 59.8%). This suggests the feasibility of the proposed analytical framework with multiple features as a means towards a more accurate assessment of mental workload in fNIRS-based BCI applications. View on the web Inoreader. Take back control of your news feed. Follow us on Twitter and Facebook.

Time-Frequency Maximal Information Coefficient Method and its Application to Functional Corticomuscular Coupling

alkiviadis.1961 shared this article with you from Inoreader Time-Frequency Maximal Information Coefficient Method and its Application to Functional Corticomuscular Coupling Via Neural Systems and Rehabilitation Engineering, IEEE Transactions on - new TOC An important challenge in the study of functional corticomuscular coupling (FCMC) is an accurate capture of the coupling relationship between the cerebral cortex and the effector muscle. The coherence method is a linear analysis method, which has certain limitations in further revealing the nonlinear coupling between neural signals. Although mutual information (MI) and transfer entropy (TE) based on information theory can capture both linear and nonlinear correlations, the equitability of these algorithms is ignored and the nonlinear components of the correlation cannot be separated. The maximal information coefficient (MIC) is a suitable method to measure the coupling between neurophysiological signals. This study extends the MIC to the time–frequency domain, named time–frequency maximal information coefficient (TFMIC), to explore the FCMC in a specific frequency band. The effectiveness, equitability, and robustness of the algorithm on the simulation data was verified and compared with coherence, TE- and MI- based methods. Simulation results showed that the TFMIC could accurately detect the coupling for different functional relationships at low noise levels. The dorsiflexion experimental results revealed that the beta-band (14–30 Hz) significant coupling was observed at channels Cz, C4, FC4, and FCz. Additionally, the results showed that the coupling was higher in the alpha-band (8–13 Hz) and beta-band (14–30 Hz) than in the gamma-band (31–45 Hz). This might be related to a transition between sensorimotor states. Specifically, the nonlinear component of FCMC was also observed at channels Cz, C4, FC4, and FCz. This study expanded the research on nonlinear coupling components in FCMC. View on the web Inoreader. Take back control of your news feed. Follow us on Twitter and Facebook.

Deep Temporal-Spatial Feature Learning for Motor Imagery-Based Brain–Computer Interfaces

alkiviadis.1961 shared this article with you from Inoreader Deep Temporal-Spatial Feature Learning for Motor Imagery-Based Brain–Computer Interfaces Via Neural Systems and Rehabilitation Engineering, IEEE Transactions on - new TOC Motor imagery (MI) decoding is an important part of brain-computer interface (BCI) research, which translates the subject's intentions into commands that external devices can execute. The traditional methods for discriminative feature extraction, such as common spatial pattern (CSP) and filter bank common spatial pattern (FBCSP), have only focused on the energy features of the electroencephalography (EEG) and thus ignored the further exploration of temporal information. However, the temporal information of spatially filtered EEG may be critical to the performance improvement of MI decoding. In this paper, we proposed a deep learning approach termed filter-bank spatial filtering and temporal-spatial convolutional neural network (FBSF-TSCNN) for MI decoding, where the FBSF block transforms the raw EEG signals into an appropriate intermediate EEG presentation, and then the TSCNN block decodes the intermediate EEG signals. Moreover, a novel stage-wise training strategy is propose d to mitigate the difficult optimization problem of the TSCNN block in the case of insufficient training samples. Firstly, the feature extraction layers are trained by optimization of the triplet loss. Then, the classification layers are trained by optimization of the cross-entropy loss. Finally, the entire network (TSCNN) is fine-tuned by the back-propagation (BP) algorithm. Experimental evaluations on the BCI IV 2a and SMR-BCI datasets reveal that the proposed stage-wise training strategy yields significant performance improvement compared with the conventional end-to-end training strategy, and the proposed approach is comparable with the state-of-the-art method. View on the web Inoreader. Take back control of your news feed. Follow us on Twitter and Facebook.

Muscle Synergy Functions for Estimating Unmeasured Muscle Excitations Using a Measured Subset

alkiviadis.1961 shared this article with you from Inoreader A Gaussian Process Model of Muscle Synergy Functions for Estimating Unmeasured Muscle Excitations Using a Measured Subset Via Neural Systems and Rehabilitation Engineering, IEEE Transactions on - new TOC Estimation of muscle excitations from a reduced sensor array could greatly improve current techniques in remote patient monitoring. Such an approach could allow continuous monitoring of clinically relevant biomechanical variables that are ideal for personalizing rehabilitation. In this paper, we introduce the notion of a muscle synergy function which describes the synergistic relationship between a subset of muscles. We develop from first principles an approximation to their behavior using Gaussian process regression and demonstrate the utility of the technique for estimating the excitation time-series of leg muscles during normal walking for nine healthy subjects. Specifically, excitations for six muscles were estimated using surface electromyography (sEMG) data during a finite time interval (called the input window) from four different muscles (called the input muscles) with mean absolute error (MAE) less than 5.0% of the maximum voluntary contraction (MVC) and that accounts for 82-88% of the variance (VAF) in the true excitations. Further, these estimated excitations informed muscle activations with less than 4.0% MAE and 89-93% VAF. We also present a detailed analysis of a number of different modeling choices, including every possible combination of four-, three- and two-muscle input sets, the size and structure of the input window, and the stationarity of the Gaussian process covariance functions. Further, application specific modifications for future use are discussed. The proposed technique lays a foundation to explore the use of reduced wearable sensor arrays and muscle synergy functions for monitoring clinically relevant biomechanics during daily life. View on the web Inoreader. Take back control of your news feed. Follow us on Twitter and Facebook.

Reducing Calibration Efforts in RSVP Tasks With Multi-Source Adversarial Domain Adaptation

alkiviadis.1961 shared this article with you from Inoreader Reducing Calibration Efforts in RSVP Tasks With Multi-Source Adversarial Domain Adaptation Via Neural Systems and Rehabilitation Engineering, IEEE Transactions on - new TOC Rapid Serial Visual Presentation (RSVP)-based Brain-Computer Interface (BCI) is an efficient information detection technology by detecting event-related brain responses evoked by target visual stimuli. However, a time-consuming calibration procedure is needed before a new user can use this system. Thus, it is important to reduce calibration efforts for BCI applications. In this article, we propose a multi-source conditional adversarial domain adaptation with the correlation metric learning (mCADA-C) framework that utilizes data from other subjects to reduce the data requirement from the new subject for training the model. This model utilizes adversarial training to enable a CNN-based feature extraction network to extract common features from different domains. A correlation metric learning (CML) loss is proposed to constrain the correlation of features based on class and domain to maximize the intra-class similarity and minimize inter-class similarity. Also, a multi-source fram ework with a source selection strategy is adopted to integrate the results of multiple domain adaptation. We constructed an RSVP-based dataset that includes 11 subjects each performing three RSVP experiments on three different days. The experimental results demonstrate that our proposed method can achieve 87.72% cross-subject balanced-accuracy under one block calibration. The results indicate our method can realize a higher performance with less calibration efforts. View on the web Inoreader. Take back control of your news feed. Follow us on Twitter and Facebook.

Beta-Range Corticomuscular Coupling Reflects Asymmetries in Hand Movement

alkiviadis.1961 shared this article with you from Inoreader Beta-Range Corticomuscular Coupling Reflects Asymmetries in Hand Movement Via Neural Systems and Rehabilitation Engineering, IEEE Transactions on - new TOC Hand movement in humans is verified as asymmetries and lateralization, and two hemispheres make some distinct but complementary contributions in the control of hand movement. However, little research has been done on whether the information transfer of the motor system is different between left and right hand movement. Considering the importance of functional corticomuscular coupling (FCMC) between the motor cortex and contralateral muscle in movement assessment, this study aimed to explore the differences between left and right hand by investigating the interaction between muscle and brain activity. Here, we applied the transfer spectral entropy (TSE) algorithm to quantize the connection between electroencephalogram (EEG) over the brain scalp and electromyogram (EMG) from extensor digitorum (ED) and flexor digitorum superficialis (FDS) muscles recorded simultaneously during a gripping task. Eight healthy subjects were enrolled in this study. Results showed that left hand yield ed narrower and lower beta synchronization compared to the right. Further analysis indicated coupling strength in EEG-EMG(FDS) combination was higher at beta band than that in EEG-EMG(ED) combination, and exhibited distinct differences between descending (EEG to EMG direction) and ascending (EMG to EEG direction) direction. This study presents the distinctions of beta-range FCMC between left and right hand, and confirms the importance of beta synchronization in understanding the mechanism of motor stability control. The cortex-muscle FCMC might be used as an evaluation approach to explore the difference between left and right movement system. View on the web Inoreader. Take back control of your news feed. Follow us on Twitter and Facebook.

Evaluation of the Nino® Two-Wheeled Power Mobility Device: A Pilot Study

alkiviadis.1961 shared this article with you from Inoreader Evaluation of the Nino® Two-Wheeled Power Mobility Device: A Pilot Study Via Neural Systems and Rehabilitation Engineering, IEEE Transactions on - new TOC Novel technologies such as the Nino® two-wheeled powered mobility device are promoted as offering an intuitive improved experience compared to conventional wheelchair mobility use. The Nino® has a smaller footprint than a power wheelchair, a zero-degree turning radius, tiller-based steering, and relies on the user leaning forwards and back to move and brake. This study aimed to evaluate manual wheelchair users' ability to use the Nino® to complete a variety of wheelchair skills, and also investigated task demand, user confidence, and user perceptions. Twelve participants with a mean of 22 years of experience using a wheelchair completed the study; most had spinal cord injuries and one had multiple sclerosis. Our findings indicate that Wheelchair Skills Test scores were significantly higher for individuals in their manual wheelchair than in the Nino®. Results from the Wheelchair Use Confidence Scale showed that confidence scores increased significantly after completing Nin o® training, and that participants were significantly more confident using their manual chair than the Nino®. Cognitive workload, as measured by the NASA-Task Load Index, was significantly higher in the Nino® than in participants' manual wheelchairs. Findings from qualitative interviews suggest that the Nino® is unlikely to be suitable as a functional replacement of an individual's manual wheelchair. Most participants felt unsafe during braking. Other perceptions included that the Nino may be a good alternative for use as a recreational outdoor mobility device, a powered mobility option to help prevent upper extremity overuse injuries, have a positive impact on social interactions, but that a high degree of focus was required during use. In addition to needing to address safety, us- bility, and functional concerns, the data suggests a clinical focus on training individuals to use these new devices may be necessary for effective community use. View on the web Inoreader. Take back control of your news feed. Follow us on Twitter and Facebook.

FPGA-Based Real-Time Simulation Platform for Large-Scale STN-GPe Network

alkiviadis.1961 shared this article with you from Inoreader FPGA-Based Real-Time Simulation Platform for Large-Scale STN-GPe Network Via Neural Systems and Rehabilitation Engineering, IEEE Transactions on - new TOC The real-time simulation of large-scale subthalamic nucleus (STN)-external globus pallidus (GPe) network model is of great significance for the mechanism analysis and performance improvement of deep brain stimulation (DBS) for Parkinson's states. This paper implements the real-time simulation of a large-scale STN-GPe network containing 512 single-compartment Hodgkin-Huxley type neurons on the Altera Stratix IV field programmable gate array (FPGA) hardware platform. At the single neuron level, some resource optimization schemes such as multiplier substitution, fixed-point operation, nonlinear function approximation and function recombination are adopted, which consists the foundation of the large-scale network realization. At the network level, the simulation scale of network is expanded using module reuse method at the cost of simulation time. The correlation coefficient between the neuron firing waveform of the FPGA platform and the MATLAB software simulation waveform is 0.9 756. Under the same physiological time, the simulation speed of FPGA platform is 75 times faster than the Intel Core i7-8700K 3.70 GHz CPU 32GB RAM computer simulation speed. In addition, the established platform is used to analyze the effects of temporal pattern DBS on network firing activities. The proposed large-scale STN-GPe network meets the need of real time simulation, which would be rather helpful in designing closed-loop DBS improvement strategies. View on the web Inoreader. Take back control of your news feed. Follow us on Twitter and Facebook.