Notes

Determining factors of eating and physical activity patterns amongst girls associated with reproductive system get older within downtown Uganda, the qualitative study.
This paper presents a neurosurgical device called NEIT 2 (Nerve Electrode Insertion Tool) to implant a 3D microelectrode array into a peripheral nervous system. Using an elastomer-made nerve holder, the device is able to stable target a flexible nerve, and then safely inserts an electrode array into the fixed nerve. Finally, a nerve containment assembly is made at once. We conducted animal experiments to evaluate the proposed scenario using a 3D printed prototype and commercial microelectrodes. The results show that microelectrodes are successfully implanted into sciatic nerves of rats and neural signals are recorded through the chronically implanted electrodes.Wirelessly powered implants are increasingly being developed as free-floating single-channel devices to interface with neurons directly at stimulation sites. In order to stimulate neurons in a manner that is safe to both the electrode and the surrounding tissue, charge accumulation over time needs to be avoided. The implementation of conventional charge balancing methods often leads to an increase in system complexity, power consumption or area, all of which are critical parameters in ultra-small wireless devices. The proposed charge balancing method described in this work, which relies on bipolar capacitive integrated electrodes, does not increase these parameters. The standalone wirelessly powered stimulating implant is implemented in a 130nm CMOS technology and measures 0.009 mm3.Microelectrodes are basic tools for investigating small-scale brain dynamics. Noble metals such as gold (Au), platinum (Pt), and iridium oxide (IrOx) have been used as an electrode material because of their biocompatibility and good charge transfer capability. Their main charge transfer mechanism is the Faradaic process with redox reactions. Unfortunately, the decrease in electrode size accelerates the irreversible electrochemical dissolution during electrical stimulation due to increased current density. The dissolution can be prevented by alternating the electrode material to capacitive charge injection materials such as titanium nitride (TiN). However, electrical conductivity of TiN is relatively lower than the noble metals, which results in a lower charge injection capability. Therefore, there is a need to increase the charge injection limit of TiN electrodes for a high-performing neurostimulation. Our previous work suggested that the Vicseck fractal design can increase the charge injection limit of the m-dominant materials, the capacitive charge injection materials could also benefit from additional investigation to fully characterize effects of electrode geometry for improved neurostimulation performance.Continuous high frequency Deep Brain Stimulation (DBS) is a standard therapy for several neurological disorders. Closed-loop DBS is expected to further improve treatment by providing adaptive, on-demand therapy. Local field potentials (LFPs) recorded from the stimulation electrodes are the most often used feedback signal in closed-loop DBS. However, closed-loop DBS based on LFPs requires simultaneous recording and stimulating, which remains a challenge due to persistent stimulation artefacts that distort underlying LFP biomarkers. Here we first investigate the nature of the stimulation-induced artefacts and review several techniques that have been proposed to deal with them. Then we propose a new method to synchronize the sampling clock with the stimulation pulse so that the stimulation artefacts are never sampled, while at the same time the Nyquist-Shannon theorem is satisfied for uninterrupted LFP recording. Test results show that this method achieves true uninterrupted artefact-free LFP recording over a wide frequency band and for a wide range of stimulation frequencies.Clinical relevance-The method proposed here provides continuous and artefact-free recording of LFPs close to the stimulation target, and thereby facilitates the implementation of more advanced closed-loop DBS using LFPs as feedback.Many studies have explored brain signals during the performance of a memory task to predict later remembered items. However, prediction methods are still poorly used in real life and are not practical due to the use of electroencephalography (EEG) recorded from the scalp. Ear-EEG has been recently used to measure brain signals due to its flexibility when applying it to real world environments. In this study, we attempt to predict whether a shown stimulus is going to be remembered or forgotten using ear-EEG and compared its performance with scalp-EEG. Our results showed that there was no significant difference between ear-EEG and scalp-EEG. In addition, the higher prediction accuracy was obtained using a convolutional neural network (pre-stimulus 74.06%, on-going stimulus 69.53%) and it was compared to other baseline methods. These results showed that it is possible to predict performance of a memory task using ear-EEG signals and it could be used for predicting memory retrieval in a practical brain-computer interface.Steady-State Visual Evoked Potentials (SSVEPs) have become one of the most used neural signals for brain- computer interfaces (BCIs) due to their stability and high signal- to-noise rate. However, the performance of SSVEP-based BCIs would degrade with a few training samples. This study was proposed to enhance the detection of SSVEP by combining the supervised learning information from training samples and the unsupervised learning information from the trial to be tested. A new method, i.e. cyclic shift trials (CST), was proposed to generate new calibration samples from the test data, which were furtherly used to create the templates and spatial filters of task- related component analysis (TRCA). The test-trial templates and spatial filters were combined with training-sample templates and spatial filters to recognize SSVEP. The proposed algorithm was tested on a benchmark dataset. As a result, it reached significantly higher classification accuracy than traditional TRCA when only two training samples were used. Speciflcally, the accuracy was improved by 9.5% for 0.7s data. Therefore, this study demonstrates CST is effective to improve the performance of SSVEP-BCI.After a spinal cord injury, a person may grasp objects using a brain-computer interface (BCI) to control a robot arm. However, most BCIs do not restore somatosensory percepts that would enable someone to sense grasp force. Intracortical microstimulation (ICMS) in the somatosensory cortex can evoke tactile sensations and may therefore offer a viable solution to provide grasp force feedback. We investigated whether a bidirectional BCI could improve grasp force control over a BCI using only visual feedback. When evaluating the error of the applied force during a force matching task, we found that ICMS feedback improved overall applied grasp force accuracy.Reinforcement learning (RL) algorithm interprets neural signals into movement intentions with the guidance of the reward in Brain-machine interfaces (BMIs). Current RL algorithms generally work for the tasks with immediate rewards delivery, and lack of efficiency in delayed reward task. Prefrontal cortex, including medial prefrontal cortex(mPFC), has been demonstrated to assign credit to intermediate steps, which reinforces preceding action more efficiently. In this paper, we propose to simulate the functionality of mPFC activities as intermediate rewards to train a RL based decoder in a two-step movement task. A support vector machine (SVM) is adopted to verify if the subject expects a reward due to the accomplishment of a subtask from mPFC activity. Then this discrimination result will be utilized to guide the training of the RL decoder for each step respectively. Here, we apply the Sarsa-style attention-gated reinforcement learning (SAGREL) as the decoder to interpret motor cortex(M1) activity to action states. We test on in vivo primary motor cortex (M1) and mPFC data collected from rats, where the rats need to first trigger the start and then press lever for rewards using M1 signals. SAGREL using intermediate rewards from mPFC activities achieves a prediction accuracy of 66.8% ± 2.0.% (mean ± std) %, which is significantly better than the one using the reward by the end of trial (45.9.% ± 1.2%). This reveals the potentials of modelling mPFC activities as intermediate rewards for the delayed reward tasks.During human standing, it has been previously observed that information about the position and frequency of visual surround motion improves balance by reducing sway responses to external disturbances. However, experimental limitations only allowed for independent investigation of such parameters while being incapable of providing a fully immersive experience of a real environment. The aim of this study is to investigate the effect of visual information on dynamic body sway in the human upright stance by presenting perturbations through a virtual reality (VR) system. Moreover, we designed a new perturbation signal based on trapezoidal velocity (TrapV) pulses enabling us to simultaneously examine the effects of amplitude and velocity on balance control. The experiments included four different peak-to-peak amplitudes (1-10 degrees), and three velocities (2-10 degree/sec). The body angle, ankle torques and shank angles were measured and analyzed in response to each perturbation. The results reveal that stimuli with higher amplitudes evoked larger responses, while they were initially increased and reached a peak, then decreased by increasing the motion velocity of visual surround.Interactions between brain and heart play an important role for sleep quality and control. However, the influence mechanism was still unclear. This study aimed to further investigate this mechanism according to build an information transfer network of brain-heart coupling. This study included 24 healthy individuals and both of them underwent overnight polysomnography. The relative spectral powers of five frequency bands and the high frequency power of heart rate variability were extracted from six electroencephalogram (EEG) channels and electrocardiography (ECG) respectively. For each EEG channel, brain-heart interaction networks were built and a directionality analysis was conducted by using multivariate transfer entropy. Results revealed the bidirectionality of information transfer between brain and heart during sleep, and the information was dominantly transfer from heart to brain. The information transfer strength between brain and heart were significantly stronger than which between frequency bands in each EEG channels. Besides, the frequency bands and EEG channels had evident influence on these interactions. This study exposed more detailed characteristics of brain-heart interaction, which will facilitate the future study about the sleep control and the diagnose of sleep related disease.In recent years, many electromyography (EMG) benchmark databases have been made publicly available to the myoelectric control research community. Many small laboratories that lack the instrumentation, access, and experience needed to collect quality EMG data have used these benchmark datasets to explore and propose new signal processing and pattern recognition algorithms. It is widely accepted that noise contamination can affect the performance of myoelectric control systems, and so useful datasets should maintain good signal quality to ensure accurate results for proposed EMG-based gesture recognition systems. Despite the availability and adoption of benchmarks datasets, however, the quality of the EMG signals in these benchmarks has not yet been examined. In this study, the signal quality of the Non-Invasive Adaptive Prosthetics (NinaPro) dataset, the most widely known publicly available benchmark database to date, was comprehensively investigated with the goals of 1) reporting the level of noise contamination in each NinaPro sub-dataset, 2) proposing signal quality criteria for assessing EMG datasets, 3) analyzing the effect of signal quality on classification performance, and 4) examining the quality of the data labels.
My Website: