Activity

A subretinal stimulator chip has been designed and tested, which combines high pixel number with highest simulation voltages, lowest power consumption, spatial peaking and illumination adaptation. A supporting ASIC completes the implantable device electronics. Blind mouse retina has successfully been stimulated in vitro.There is an increasing demand for real-time neural signal monitoring from a large number of electrode contacts to provide adequate spatial and temporal resolution for brain mapping and high-resolution neural interface. This paper proposes a novel multi-channel neural recording system that records neural signals from a large number of electrodes with a smaller number of recording channels. The system utilizes an adaptive electrode selection technique to automatically scan the electrode arrays and record from selected electrodes where neural spikes are detected. The proposed neural recording IC was fabricated in CMOS 180 nm process and tested with in vitro environments. Experiment results with pre-recorded neural data indicate that neural spikes can be separated and amplified with the proposed system and counted in real-time.Micro Bio Processor version 1.5 (MBPv15) Development Kit is specially engineered to support various function-alities of implantable devices such as bio-signal sensing, neural stimulation, and dual-band wireless connectivity & charging. It provides a convenient way to evaluate the MBPv15 chip solution as a system component by a modular design of hardware and software. As a result, MBPv15 chip solution enables to develop wireless neural implants in a mm-scale form factor with ultra-low power consumption by achieving 1.6 mW for neural spike detection and 9.8 mW for neural stimulation, respectively.In this paper, a power efficient, low-noise and high swing capacitively-coupled amplifier (CCA) for neural recording applications is proposed. The use of current splitting technique and current scaling technique in a current mirror operational transconductance amplifier (CM-OTA) has lead to a very good trade-off between power and noise. The presented architecture is simple, without cascode transistor while it has more than 80 dB open-loop gain without extra power consumption. As a result, the proposed structure has a better power efficiency factor (PEF) and output swing in comparison with previous reported architectures is increased to the 2Vov below the maximum supply voltage. Androgen Receptor Antagonist In order to reduce flicker noise and achieve better trade-off between the power and noise, PMOS transistors with an optimum size have been utilized which operate in sub-threshold region. The amplifier is designed and simulated in a commercially available 0.18 μm CMOS technology. Monte Carlo simulations for process and mismatch have been carried out. The gain of the proposed amplifier is 39.22 dB in its bandwidth (3 Hz – 5 kHz). Total input-referred noise is 3.03 μVrms over 1 Hz – 10 kHz. The power consumption of the amplifier is 2.98 μW at supply voltage of 1.4 V. The noise efficiency factor (NEF) and PEF are 2.4 and 8.06, respectively. The output swing is about 1.16 V. It means the proposed amplifier can tolerate up to 13.2 mV peak-to-peak input signal while its total harmonic distortion (THD) is less than 1%.Sleep disturbance and deprivation are major factors in delayed recovery, which can affect both the physical and emotional well-being of patients. Patients hospitalized in the Intensive Care Unit (ICU) are especially vulnerable to sleep deprivation due to light-induced disturbances. A desirable lighting intervention in the ICU would minimize light-induced disturbances while simultaneously providing feedback for the staff on when to perform patient care activities that require high intensity lighting. To this end, we performed a first phase testing for a biometrics-integrated lighting system that serves a dual function of sleep initiation and maintenance to improve the patient’s quality of sleep. Preliminary findings are presented as a case study to assess the feasibility of scaling up the experimental model. While findings point to additional testing being necessary to determine whether the lighting system will be effective, the experiment detailed in this report establishes a starting paradigm upon which to base further investigation.Clinical Relevance- A biometrics-integrated lighting system that can improve sleep quality of the patient will not only reduce cost of care for the patients, but also increase the level of satisfaction for both patients and the hospital staff.Fibromyalgia is a musculoskeletal disorder characterized by chronic, widespread muscle pain. This condition is associated with disturbed sleep, which has a direct impact on patient quality of life. Patient-reported outcomes are frequently used to assess sleep quality, but show modest correlations with objective measures of sleep, such as polysomnography. Working towards our goal of an automated ambulatory system of assessing sleep quality, we use features from blood volume pulse (BVP) and electrodermal activity (EDA) collected with a wearable device during sleep. We compare these measurements between individuals with fibromyalgia who experienced poor sleep and individuals in a control group who experienced refreshing sleep. By applying Learning Using Concave and Convex Kernels (LUCCK) and Support Vector Machines (SVM), we achieve mean Area Under the Receiver Operating Characteristic Curve (AUC) of 0.6573 and 0.6526, respectively, by using BVP data for classifying individuals to the two groups.A sleep inertia after waking up strongly affects the overall mental recovery after sleep. The sleep inertia depends not as much on overall sleep quality but also on the sleep stage in the waking up moment. The fix-time alarming system results in waking up at random sleep stage, which results in frequent sleep inertia. The widely used flow-time alarming systems based on motion detection (actigraphy) reduce but do not eliminate sleep inertia. Such systems do not wake up users in Deep sleep stage, but may instead wake them up in Wake, Light, or REM (Rapid Eyes Movement) stages. Moreover, frequent waking up in the REM stage results in serious psychological issues. We present a smartwatch alarm system that predicts sleep stages and thus produces an alarm call at an easy waking up moment with minimal sleep inertia effect. The sleep stages are predicted using an Encoder-Decoder Recurrent Neural Network model. The rationale of the prediction is that each sleep stages cycling pattern is a continuous quasi-periodic process.