Interhemispheric Functional Reorganization and its Structural Base After BCI-Guided Upper-Limb Training in Chronic Stroke

alkiviadis.1961 shared this article with you from Inoreader Interhemispheric Functional Reorganization and its Structural Base After BCI-Guided Upper-Limb Training in Chronic Stroke Via Neural Systems and Rehabilitation Engineering, IEEE Transactions on - new TOC Brain–computer interface (BCI)-guided robot-assisted upper-limb training has been increasingly applied to stroke rehabilitation. However, the induced long-term neuroplasticity modulation still needs to be further characterized. This study investigated the functional reorganization and its structural base after BCI-guided robot-assisted training using resting-state fMRI, task-based fMRI, and diffusion tensor imaging (DTI) data. The clinical improvement and the neurological changes before, immediately after, and six months after 20-session BCI-guided robot hand training were explored in 14 chronic stroke subjects. The structural base of the induced functional reorganization and motor improvement were also investigated using DTI. Repeated measure ANOVA indicated long-term motor improvement was found (F[2, 26] = 6.367, p = 0.006). Significantly modulated functional connectivity (FC) was observed between ipsilesional motor regions (M1 and SMA) and some contralesional areas (SMA, P Md, SPL) in the seed-based analysis. Modulated FC with ipsilesional M1 was significantly correlated with motor function improvement (r = 0.6455, p = 0.0276). Besides, increased interhemispheric FC among the sensorimotor area from resting-state data and increased laterality index from task-based data together indicated the re-balance of the two hemispheres during the recovery. Multiple linear regression models suggested that both motor function improvement and the functional change between ipsilesional M1 and contralesional premotor area were significantly associated with the ipsilesional corticospinal tract integrity. The results in the current study provided solid support for stroke recovery mechanism in terms of interhemispheric interaction and its structural substrates, which could further enhance the understanding of BCI training in stroke rehabilitation. This- study was registered at https://clinicaltrials.gov (NCT02323061). View on the web Inoreader. Take back control of your news feed. Follow us on Twitter and Facebook.

Functional Brain Connectivity Analysis in Intellectual Developmental Disorder During Music Perception

alkiviadis.1961 shared this article with you from Inoreader Functional Brain Connectivity Analysis in Intellectual Developmental Disorder During Music Perception Via Neural Systems and Rehabilitation Engineering, IEEE Transactions on - new TOC Intellectual Developmental Disorder (IDD) is a neurodevelopmental disorder involving impairment of general cognitive abilities. This disorder impacts the conceptual, social, and practical skills adversely. There is a growing interest in exploring the neurological behavior associated with these disorders. Assessment of functional brain connectivity and graph theory measures have emerged as powerful tools to aid these research goals. The current research contributes by comparing brain connectivity patterns of IDD individuals to those typical controls. Considering the intellectual deficits linked to the IDD population, we hypothesized an atypical connectivity pattern in the IDD group. Brain signals were recorded by a dry-electrode Electroencephalography (EEG) system during the rest and music states observed by the subjects. We studied a group of seven IDD subjects and seven healthy controls to understand the connectivity within the human brain during the resting-state vis-à-vis w hile listening to music. Findings of this research emphasize (1) hyper-connected functional brain networks and increased modularity as potential characteristics of the IDD group, (2) the ability of soothing music to reduce the resting state hyper-connected pattern in the IDD group, and (3) the effect of soothing music in the lower frequency bands of the control group compared to the higher frequency bands of the IDD group. View on the web Inoreader. Take back control of your news feed. Follow us on Twitter and Facebook.

Resilient EMG Classification to Enable Reliable Upper-Limb Movement Intent Detection

alkiviadis.1961 shared this article with you from Inoreader Resilient EMG Classification to Enable Reliable Upper-Limb Movement Intent Detection Via Neural Systems and Rehabilitation Engineering, IEEE Transactions on - new TOC Reliable control of assistive devices using surface electromyography (sEMG) remains an unsolved task due to the signal's stochastic behavior that prevents robust pattern recognition for real-time control. Non-representative samples lead to inherent class overlaps that generate classification ripples for which the most common alternatives rely on post-processing and sample discard methods that insert additional delays and often do not offer substantial improvements. In this paper, a resilient classification pipeline based on Extreme Learning Machines (ELM) was used to classify 17 different upper-limb movements through sEMG signals from a total of 99 trials derived from three different databases. The method was compared to a baseline ELM and a sample discarding (DISC) method and proved to generate more stable and consistent classifications. The average accuracy boost of ≈ 10% in all databases lead to average weighted accuracy rates higher as 53,4% for amputees and 89,0% for n on-amputee volunteers. The results match or outperform related works even without sample discards. View on the web Inoreader. Take back control of your news feed. Follow us on Twitter and Facebook.

If you want to be happy, make someone else happy. If you want to find the right person in your life, be the right person. If you want to see change in the world, become the change you want to see. Deepak Chopra

alkiviadis.1961 shared this article with you from Inoreader Zero-Shot Learning for EEG Classification in Motor Imagery-Based BCI System Via Neural Systems and Rehabilitation Engineering, IEEE Transactions on - new TOC A brain-computer interface (BCI) based on motor imagery (MI) translates human intentions into computer commands by recognizing the electroencephalogram (EEG) patterns of different imagination tasks. However, due to the scarcity of MI commands and the long calibration time, using the MI-based BCI system in practice is still challenging. Zero-shot learning (ZSL), which can recognize objects whose instances may not have been seen during training, has the potential to substantially reduce the calibration time. Thus, in this context, we first try to use a new type of motor imagery task, which is a combination of traditional tasks and propose a novel zero-shot learning model that can recognize both known and unknown categories of EEG signals. This is achieved by first learning a non-linear projection from EEG features to the target space and then applying a novelty detection method to differentiate unknown classes from known classes. Applications to a dataset collected from nine subj ects confirm the possibility of identifying a new type of motor imagery only using already obtained motor imagery data. Results indicate that the classification accuracy of our zero-shot based method accounts for 91.81% of the traditional method which uses all categories of data. View on the web Inoreader. Take back control of your news feed. Follow us on Twitter and Facebook.

The art of medicine consists in amusing the patient while nature cures the disease. Voltaire

alkiviadis.1961 shared this article with you from Inoreader Automatic Diagnosis of Cerebral Palsy Gait Using Computational Intelligence Techniques: A Low-Cost Multi-Sensor Approach Via Neural Systems and Rehabilitation Engineering, IEEE Transactions on - new TOC Automatic diagnosing of Cerebral Palsy (CP) gait is crucial in quantitative evaluation of a therapeutic intervention. Existing systems for such gait assessment are expensive and require user intervention. This study proposes a low-cost gait assessment system equipped with multiple Kinect sensors. Forty subjects (20 CP patients and 20 normal) were recruited for the experiment. To remove outlier frames from the combined gait signal of multiple sensors a data driven algorithm was proposed. Different supervised classifiers along with extreme learning machine were investigated to diagnose CP gait. In addition, a feature level analysis was also performed. Several spatio-temporal features (i.e. step length, stride length, stride time, etc.) were extracted. The strength of walking ratio, a speed invariant feature, to detect CP gait was thoroughly analyzed. The proposed system outperformed state-of-the-art with ≈98% of accuracy (sensitivity: 100%, and specificity: 96.87%). Results ind icate a substantial improvement in abnormality detection performance after outlier removal. Based on ReliefF feature ranking algorithm, walking ratio ranked the best among other classical gait features. Performance of all classifiers increased substantially using walking ratio as a feature. Extreme learning machine demonstrated a competing performance in all cases. The higher classification accuracy of this low-cost system using only a single feature makes it attractive for CP gait detection. View on the web Inoreader. Take back control of your news feed. Follow us on Twitter and Facebook.

Automatic Identification of High-Risk Autism Spectrum Disorder: A Feasibility Study Using Video and Audio Data Under the Still-Face Paradigm

alkiviadis.1961 shared this article with you from Inoreader Automatic Identification of High-Risk Autism Spectrum Disorder: A Feasibility Study Using Video and Audio Data Under the Still-Face Paradigm Via Neural Systems and Rehabilitation Engineering, IEEE Transactions on - new TOC It is reported that the symptoms of autism spectrum disorder (ASD) could be improved by effective early interventions, which arouses an urgent need for large-scale early identification of ASD. Until now, the screening of ASD has relied on the child psychiatrist to collect medical history and conduct behavioral observations with the help of psychological assessment tools. Such screening measures inevitably have some disadvantages, including strong subjectivity, relying on experts and low-efficiency. With the development of computer science, it is possible to realize a computer-aided screening for ASD and alleviate the disadvantages of manual evaluation. In this study, we propose a behavior-based automated screening method to identify high-risk ASD (HR-ASD) for babies aged 8–24 months. The still-face paradigm (SFP) was used to elicit baby's spontaneous social behavior through a face-to-face interaction, in which a mother was required to maintain a normal interaction to amuse her baby for 2 minutes (a baseline episode) and then suddenly change to the no-reaction and no-expression status with 1 minute (a still-face episode). Here, multiple cues derived from baby's social stress response behavior during the latter episode, including head-movements, facial expressions and vocal characteristics, were statistically analyzed between HR-ASD and typical developmental (TD) groups. An automated identification model of HR-ASD was constructed based on these multi-cue features and the support vector machine (SVM) classifier; moreover, its screening performance was satisfied, for all the accuracy, specificity and sensitivity exceeded 90% on the cases included in this study. The experimental results suggest its feasibility in the early screening of HR-ASD. View on the web Inoreader. Take back control of your news feed. Follow us on Twitter and Facebook.

Mibefradil and Flunarizine, Two T-Type Calcium Channel Inhibitors, Protect Mice against Lipopolysaccharide-Induced Acute Lung Injury

alkiviadis.1961 shared this article with you from Inoreader Mibefradil and Flunarizine, Two T-Type Calcium Channel Inhibitors, Protect Mice against Lipopolysaccharide-Induced Acute Lung Injury Via Hindawi Publishing Corporation Recent studies have illuminated that blocking Ca2+ influx into effector cells is an attractive therapeutic strategy for lung injury. We hypothesize that T-type calcium channel may be a potential therapeutic target for acute lung injury (ALI). In this study, the pharmacological activity of mibefradil (a classical T-type calcium channel inhibitor) was assessed in a mouse model of lipopolysaccharide- (LPS-) induced ALI. In LPS challenged mice, mibefradil (20 and 40 mg/kg) dramatically decreased the total cell number, as well as the productions of TNF-α and IL-6 in bronchoalveolar lavage fluid (BALF). Mibefradil also suppressed total protein concentration in BALF, attenuated Evans blue extravasation, MPO activity, and NF-κB activation in lung tissue. Furthermore, flunarizine, a widely prescripted antimigraine ag ent with potent inhibition on T-type channel, was also found to protect mice against lung injury. These data demonstrated that T-type calcium channel inhibitors may be beneficial for treating acute lung injury. The important role of T-type calcium channel in the acute lung injury is encouraged to be further investigated. View on the web Inoreader. Take back control of your news feed. Follow us on Twitter and Facebook.

Effects of Rhein on Bile Acid Homeostasis in Rats

alkiviadis.1961 shared this article with you from Inoreader Effects of Rhein on Bile Acid Homeostasis in Rats Via Hindawi Publishing Corporation Rhein, the active ingredient of rhubarb, a medicinal and edible plant, is widely used in clinical practice. However, the effects of repeated intake of rhein on liver function and bile acid metabolism are rarely reported. In this work, we investigated the alterations of 14 bile acids and hepatic transporters after rats were administered with rhein for 5 weeks. There was no obvious injury to the liver and kidney, and there were no significant changes in biochemical indicators. However, 1,000 mg/kg rhein increased the liver total bile acid (TBA) levels, especially taurine-conjugated bile acids (t-CBAs), inhibited the expression of farnesoid X receptor (FXR), small heterodimer partner (SHP), and bile salt export pump (BSEP) mRNA, and upregulated the expression of (cholesterol 7α-hydroxylase) CYP7A1 mRNA. Rhein close to the clinical dose (10 mg/kg and 30 mg/kg) reduced the amounts of TBAs, especially unconjugated bile acids (UCBAs), and elevated the expression of FXR and mult idrug resistance-associated protein 3 (Mrp3) mRNA. These results denote that rhein is relatively safe to use at a reasonable dose and timing. 30 mg/kg rhein may promote bile acid transport and reduce bile acid accumulation by upregulating the expression of FXR mRNA and Mrp3 mRNA, potentially resulting in the decrease in serum UBCAs. View on the web Inoreader. Take back control of your news feed. Follow us on Twitter and Facebook.

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.