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At a minimum power consumption of 87 μW, the LDC still achieves 95 dB DR. The LDC is also validated with on-body PPG and NIRS measurement by using a photodiode (PD) and a silicon photomultiplier (SIPM), respectively.In the past few years it has been demonstrated that electroencephalography (EEG) can be recorded from inside the ear (in-ear EEG). To open the door to low-profile earpieces as wearable brain-computer interfaces (BCIs), this work presents a practical in-ear EEG device based on multiple dry electrodes, a user-generic design, and a lightweight wireless interface for streaming data and device programming. The earpiece is designed for improved ear canal contact across a wide population of users and is fabricated in a low-cost and scalable manufacturing process based on standard techniques such as vacuum forming, plasma-treatment, and spray coating. A 2.5 × 2.5 cm2 wireless recording module is designed to record and stream data wirelessly to a host computer. Performance was evaluated on three human subjects over three months and compared with clinical-grade wet scalp EEG recordings. Recordings of spontaneous and evoked physiological signals, eye-blinks, alpha rhythm, and the auditory steady-state response (ASSR), are presented. This is the first wireless in-ear EEG to our knowledge to incorporate a dry multielectrode, user-generic design. The user-generic ear EEG recorded a mean alpha modulation of 2.17, outperforming the state-of-the-art in dry electrode in-ear EEG systems.In this work, a dual-mode stimulus chip with a built-in high voltage generator was proposed to offer a broad-range current or voltage stimulus patterns for biomedical applications. With an on-chip and built-in high voltage generator, this stimulus chip could generate the required high voltage supply without additional supply voltage. With a nearly 20 V operating voltage, the overstress and reliability issues of the stimulus circuits were thoroughly considered and carefully addressed in this work. This stimulus system only requires an area of 0.22 mm2 per single channel and is fully on-chip implemented without any additional external components. The dual-mode stimulus chip was fabricated in a 0.25-μm 2.5V/5V/12V CMOS (complementary metal-oxide-semiconductor) process, which can generate the biphasic current or voltage stimulus pulses. #link# The current level of stimulus is up to 5 mA, and the voltage level of stimulus can be up to 10 V. Moreover, this chip has been successfully applied to stimulate a guinea pig in an animal experiment. The proposed dual-mode stimulus system has been verified in electrical tests and also demonstrated its stimulation function in animal experiments.Magnetomyography (MMG) with superconducting quantum interference devices (SQUIDs) enabled the measurement of very weak magnetic fields (femto to pico Tesla) generated from the human skeletal muscles during contraction. However, SQUIDs are bulky, costly, and require working in a temperature-controlled environment, limiting wide-spread clinical use. We introduce a low-profile magnetoelectric (ME) sensor with analog frontend circuitry that has sensitivity to measure pico-Tesla MMG signals at room temperature. It comprises magnetostrictive and piezoelectric materials, FeCoSiB/AlN. Accurate device modelling and simulation are presented to predict device fabrication process comprehensively using the finite element method (FEM) in COMSOL Multiphysics. The fabricated ME chip with its readout circuit was characterized under a dynamic geomagnetic field cancellation technique. The ME sensor experiment validate a very linear response with high sensitivities of up to 378 V/T driven at a resonance frequency of fres = 7.76 kHz. Measurements show the sensor limit of detections of down to 175 pT/√Hz at resonance, which is in the range of MMG signals. Such a small-scale sensor has the potential to monitor chronic movement disorders and improve the end-user acceptance of human-machine interfaces.In this article, we present a real-time electroencephalogram (EEG) based depth of anesthesia (DoA) monitoring system in conjunction with a deep learning framework, AnesNET. An EEG analog front-end (AFE) that can compensate ±380-mV electrode DC offset using a coarse digital DC servo loop is implemented in the proposed system. The EEG-based MAC, EEGMAC, is introduced as a novel index to accurately predict the DoA, which is designed for applying to patients anesthetized by both volatile and intravenous agents. The proposed deep learning protocol consists of four layers of convolutional neural network and two dense layers. In addition, we optimize the complexity of the deep neural network (DNN) to operate on a microcomputer such as the Raspberry Pi 3, realizing a cost-effective small-size DoA monitoring system. Fabricated in 110-nm CMOS, the prototype AFE consumes 4.33 μW per channel and has the input-referred noise of 0.29 μVrms from 0.5 to 100 Hz with the noise efficiency factor of 2.2. The proposed DNN was evaluated with pre-recorded EEG data from 374 subjects administrated by inhalational anesthetics under surgery, achieving an average squared and absolute errors of 0.048 and 0.05, respectively. The EEGMAC with subjects anesthetized by an intravenous agent also showed a good agreement with the bispectral index value, confirming the proposed DoA index is applicable to both anesthetics. The implemented monitoring system with the Raspberry Pi 3 estimates the EEGMAC within 20 ms, which is about thousand-fold faster than the BIS estimation in literature.Neurons are the primary building block of the nervous system. Exploring the mysteries of the brain in science or building a novel brain-inspired hardware substrate in engineering are inseparable from constructing an efficient biological neuron. Balancing BRM/BRG1 ATP Inhibitor-1 price and the implementation cost of a neuron is a grand challenge in neuromorphic field. In this paper, we present a low-cost adaptive exponential integrate-and-fire neuron, called SC-AdEx, for large-scale neuromorphic systems using stochastic computing. In the proposed model, arithmetic operations are performed on stochastic bit-streams with small and low-power circuitry. To evaluate the proposed neuron, we perform biological behavior analysis, including various firing patterns. Furthermore, the model is synthesized and implemented physically on FPGA as a proof of concept. Experimental results show that our model can precisely reproduce wide range biological behaviors as the original model, with higher computational performance and lower hardware cost against state-of-the-art AdEx hardware neurons.