The cold face test and cold pressor test were performed to physiologically activate ANS. The SKNA and aVNA can be obtained simultaneously, and they are correlated with the heart rate change during the physiological challenge. The aVNA has a high potential to be developed into a novel method to measure the PNS activity.After a stroke, individuals often exhibit upper extremity (UE) motor dysfunction, influencing the performance of everyday tasks. Characterizing UE movements is useful to track recovery and response to intervention. Yet, due to the complexity of the recovery process, UE movements may be extremely variable and person-specific. While this renders automatic recognition of these gestures challenging, machine learning methods could be used to classify UE movements in atypical populations. PU-H71 HSP (HSP90) inhibitor In the current study, we utilize data from 20 individuals post-stroke and 20 age-matched controls to identify an optimal set of sensor-extracted features for the classification of unimanual and bimanual gestures during task performance. We found that using fewer than 100 features along with a random forest classifier produced the best performance across both groups, with both user-dependent and user-independent models.Parkinson's Disease (PD) is the second most common neurodegenerative disorder with the non-motor symptoms preceding the motor impairment that is needed for clinical diagnosis. In the current study, an angle-based analysis that processes activity data during sleep from a smartwatch for quantification of sleep quality, when applied on controls and PD patients, is proposed. Initially, changes in their arm angle due to activity are captured from the smartwatch triaxial accelerometry data and used for the estimation of the corresponding binary state (awake/sleep). Then, sleep metrics (i.e., sleep efficiency index, total sleep time, sleep fragmentation index, sleep onset latency, and wake after sleep onset) are computed and used for the discrimination between controls and PD patients. A process of validation of the proposed approach when compared with the PSG-based ground truth in an in-the-clinic setting, resulted in comparable state estimation. Moreover, data from 15 early PD patients and 11 healthy controls were used as a test set, including 1,376 valid sleep recordings in-the-wild setting. The univariate analysis of the extracted sleep metrics achieved up to 0.77 AUC in early PD patients vs. healthy controls classification and exhibited a statistically significant correlation (up to 0.46) with the clinical PD Sleep Scale 2 counterpart Items. The findings of the proposed method show the potentiality to capture non-motor behavior from users' nocturnal activity to detect PD in the early stage.This work presents a modular, light-weight head-borne neuromodulation platform that achieves low-power wireless neuromodulation and allows real-time programmability of the stimulation parameters such as the frequency, duty cycle, and intensity. This platform is comprised of two parts the main device and the optional intensity module. The main device is functional independently, however, the intensity control module can be introduced on demand. The stimulation is achieved through the use of energy-efficient µLEDs directly integrated in the custom-drawn fiber-based probes. Our platform can control up to 4 devices simultaneously and each device can control multiple LEDs in a given subject. Our hardware uses off-the-shelf components and has a plug and play structure, which allows for fast turn-over time and eliminates the need for complex surgeries. The rechargeable, battery-powered wireless platform uses Bluetooth Low Energy (BLE) and is capable of providing stable power and communication regardless of orientation. This presents a potential advantage over the battery-free, fully implantable systems that rely on wireless power transfer, which is typically direction-dependent, requires sophisticated implantation surgeries, and demands complex custom-built experimental apparatuses. Although the battery life is limited to several hours, this is sufficient to complete the majority of behavioral neuroscience experiments. Our platform consumes an average power of 0.5 mW, has a battery life of 12 hours.This paper investigates the effectiveness of four Huffman-based compression schemes for different intracortical neural signals and sample resolutions. The motivation is to find effective lossless, low-complexity data compression schemes for Wireless Intracortical Brain-Machine Interfaces (WI-BMI). The considered schemes include pre-trained Lone 1st and 2nd order encoding [1], pre-trained Delta encoding, and pre-trained Linear Neural Network Time (LNNT) encoding [2]. Maximum codeword-length limited versions are also considered to protect against overfit to training data. The considered signals are the Extracellular Action Potential signal, the Entire Spiking Activity signal, and the Local Field Potential signal. Sample resolutions of 5 to 13 bits are considered. The result show that overfit-protection dramatically improves compression, especially at higher sample resolutions. Across signals, 2nd order encoding generally performed best at lower sample resolutions, and 1st order, Delta and LNNT encoding performed best at higher sample resolutions. The proposed methods should generalise to other remote sensing applications where the distribution of the sensed data can be estimated a priori.Advanced polymer science and design technologies are constantly evolving to meet ever-growing expectations for flexible optical MEMS. In this work, we present design and microfabrication considerations for designed flexible Polymeric Opto-Electro-Mechanical Systems (POEMS). The presented methods integrate waveguide fabrication and laser diode (LD) chip assembly with Lawrence Livermore National Laboratory's (LLNL's) flexible thin-film technology to enable LLNL's first neural optoelectrode that can deliver guided light for neural activation. We support our findings with electrical and optical bench verification tests, present thermal simulation models to analyze heat dissipation of laser light sources on polymer substrates and discuss potential modifications for next generation prototypes. This fully integrated approach will allow spatial precision, scalability and more particularly, longer lifetime, needed to enable chronic studies of brain activities.