Neural correlates of intentionally induced human emotions may offer alternative imagery strategies to control brain-computer interface (BCI) applications. In this paper, a novel BCI control strategy i.e., imagining fictional or recalling mnemonic sad and happy events, emotion-inducing imagery (EII), is compared to motor imagery (MI) in a study involving multiple sessions using a two-class electroencephalogram (EEG)-based BCI paradigm with 12 participants. The BCI setup enabled online continuous visual feedback presentation in a game involving one-dimensional control of a game character. MI and EII are compared across different signal-processing frameworks which are based on neural-time-series-prediction-preprocessing (NTSPP), filter bank common spatial patterns (FBCSP) and hemispheric asymmetry (ASYM). Online single-trial classification accuracies (CA) results indicate that MI performance across all participants is 77.54% compared to EII performance of 68.78% (p 70%) observed for some participants.This study investigated the effects of low-intensity transcranial ultrasound stimulation (TUS) on behavior in a mouse model of Parkinson’s disease (PD) induced by 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP). The PD mouse model was induced by consecutive injecting the mice with MPTP for 7 days. When the animal model is completed, we performed behavioral tests including the wire hanging test, open field test and forced swimming test on day 1, 2, 3, 4, 7, 14 during 2 weeks. Simultaneously, the ultrasound was used to stimulate the brain tissue of the mice daily for these 2 weeks. The data were analyzed to examine treatment effects. When the PD+TUS and PD+Sham groups were compared, the behavior of the PD+TUS mice was better on the fourth day after TUS (*p less then 0.05) and had further improved on the fourteenth day of TUS (**p less then 0.01). These results demonstrate that TUS can improve behavior in mice with MPTP-induced PD. The treatment effect gradually improved as the TUS duration increased.Previous clinical studies have reported that gait retraining is an effective non-invasive intervention for patients with medial compartment knee osteoarthritis. These gait retraining programs often target a reduction in the knee adduction moment (KAM), which is a commonly used surrogate marker to estimate the loading in the medial compartment of the tibiofemoral joint. However, conventional evaluation of KAM requires complex and costly equipment for motion capture and force measurement. Gait retraining programs, therefore, are usually confined to a laboratory environment. In this study, machine learning techniques were applied to estimate KAM during walking with data collected from two low-cost wearable sensors. When compared to the traditional laboratory-based measurement, our mobile solution using artificial neural network (ANN) and XGBoost achieved an excellent agreement with R2 of 0.956 and 0.947 respectively. find more With the implementation of a real-time audio feedback system, the present algorithm may provide a viable solution for gait retraining outside laboratory. Clinical treatment strategies can be developed using the continuous feedback provided by our system.We characterized the passive mechanical properties of the affected and contralateral musculotendon units in 9 chronic stroke survivors as well as in 6 neurologically intact controls. Using a position-controlled motor, we precisely indented the distal tendon of the biceps brachii to a 20 mm depth from skin, recording both its sagittal motion using ultrasound movies and the compression force at the tip of the indenter. Length changes of 8 equally-spaced features along the aponeurosis axis were quantified using a pixel-tracking protocol. We report that, on the aggregate and with respect to contralateral and control, respectively, the affected side initiates feature motion at a shorter indentation distance by 61% and 50%, travels further by 15% and 9%, at a lower rate of 28% and 15%, and is stiffer by 40% and 57%. In an extended analysis including the spatial location of the 8 designated features, we report that in contrast to the contralateral and control muscles, the affected musculotendon unit does not strain measurably within the imaging window. These results confirm that chronic stroke-induced spasticity changes musculotendon unit passive mechanics, causing it to not strain under stretch. The mechanisms responsible for altered passive mechanics may lie within extracellular matrix fibrosis.We present and report on Design Exposition Discussion Documents (DExDs), a new means of fostering collaboration between visualization designers and domain experts in applied visualization research. DExDs are a collection of semi-interactive web-based documents used to promote design discourse to communicate new visualization designs, and their underlying rationale, and to elicit feedback and new design ideas. Developed and applied during a four-year visual data analysis project in criminal intelligence, these documents enabled a series of visualization re-designs to be explored by crime analysts remotely — in a flexible and authentic way. The DExDs were found to engender a level of engagement that is qualitatively distinct from more traditional methods of feedback elicitation, supporting the kind of informed, iterative and design-led feedback that is core to applied visualization research. They also offered a solution to limited and intermittent contact between analyst and visualization researcher and began to address more intractable deficiencies, such as social desirability-bias, common to applied visualization projects. Crucially, DExDs conferred to domain experts greater agency over the design process — collaborators proposed design suggestions, justified with design knowledge, that directly influenced the re-redesigns. We provide context that allows the contributions to be transferred to a range of settings.Boundary detection has long been a fundamental tool for image processing and computer vision, supporting the analysis of static and time-varying data. In this work, we built upon the theory of Graph Signal Processing to propose a novel boundary detection filter in the context of graphs, having as main application scenario the visual analysis of spatio-temporal data. More specifically, we propose the equivalent for graphs of the so-called Laplacian of Gaussian edge detection filter, which is widely used in image processing. The proposed filter is able to reveal interesting spatial patterns while still enabling the definition of entropy of time slices. The entropy reveals the degree of randomness of a time slice, helping users to identify expected and unexpected phenomena over time. The effectiveness of our approach appears in applications involving synthetic and real data sets, which show that the proposed methodology is able to uncover interesting spatial and temporal phenomena. The provided examples and case studies make clear the usefulness of our approach as a mechanism to support visual analytic tasks involving spatio-temporal data.