Shepherd Penn (cheeksalary21)
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. 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
