This paper presents a 0.13-μm CMOS system-on-chip (SoC) designed for multilead ECG recording in low-power wearable applications. The fully integrated system features eight fully differential recording channels, capable of extracting 11 ECG leads, a fully differential 10-bit capacitive successive approximation register (SAR) analog-to-digital converter (ADC), a digital controller with a custom master SPI interface, low-dropout regulators (LDOs), and level shifters to facilitate integration. The ECG recording channels are implemented using a novel fully differential current mirror-based bioamplifier circuit, which offers tunable gain and intrinsic band-pass filtering. The bioamplifier achieves an input-referred noise of 1.8 μVrmsover a 0.3-500 Hz bandwidth, a total harmonic distortion (THD) of 0.234%, and consumes 4.24 μA. The ADC achieves an effective number of bits (ENOB) of 9.18 while consuming 11 μA. Overall, the SoC draws 347 μW from a 1.2-V supply and demonstrates successful 11-lead ECG recordings and PQRST wave identification.

This paper presents an ultra-wideband (UWB) transmitter based on capacitive array that decreases dependency of data rate to pulse repetition frequency, as well as power consumption and complexity. The entire system includes several delay stages, a capacitive array circuit, a Schmitt trigger, an impulse generator, a cross-coupled oscillator, and an antenna driver. A sequence of 5-bit parallel data is applied to the capacitive array, providing ramp signals with 32 equally separated slopes. This returns a variable pulsewidth at the output of the Schmitt trigger circuit, which corresponds to a specific sequence of input data. Post-layout simulation results show that the proposed circuit provides a linear time change in the pulsewidth with an accuracy of 176 ps in average for every input data LSB. Furthermore, the entire circuit consumes only 190 µW from a 0.6-V supply. The proposed transmitter achieves a significantly low energy consumption of 950 fJ/bit at 200 Mbps within the Federal Communications Commission spectral mask which addresses the design challenges of ultralow-power internet-of-things devices. The circuit is designed in TSMC 65-nm standard CMOS technology and occupies 0.0525 mm2of die area.

This work presents a system for measuring and analyzing motion, by a portable electronic device and a flexible fiber sensor. The fiber is composed of multi-walled carbon nanotubes (MWCNTs) for its conductivity and polydimethylsiloxane elastomer (PDMS) for its elasticity. A new sensor interface circuit was designed in this study to interface with the fiber and measure its impedance. The measured impedance data are sampled and transmitted via Bluetooth to a laptop. The characteristics of the fiber and a wireless measurement system allow an easy integration into a smart garment to monitor various vital signs and motion markers (e.g angle, step). The system was assessed on a robotic arm before being put in realistic situations through various exercises (flexion/extension knee movements, standing multi-joint movements and walk/run on treadmill) on 5 participants for its ability to measure angle, number of movements, rate and speed. In addition, fibers measurement endurance capacities over months were observed. An assessment of the fiber impedance measurement circuit was performed (minimum resolution of$25~\Omega $, relative error of 2.82% on the estimated value of resistance). Tests carried out over a period of several months show that the fiber maintained good measurement performance when tested on a robotic arm, given an average correlation of 0.85 between angle and fiber impedance. The relative error (RE) made on the number of detected movements was 6.57% in average. In realistic workout situations, these values respectively reached between 0.58 and 73.95% for flexion/extension knee movements. A correlation factor of 0.76 was obtained when the participants were walking on a treadmill at a given speed. Otherwise, RE on number of movements was 8.33% for treadmill exercise, 12.84% on standing exercise. For these exercises and for the movement rate, the average correlation calculated with the reference was between 0.75 and 0.33. Finally, RE on estimated speed was 23.3% in average for the treadmill exercise. The system (sensor interface circuit and Fiber) allows to properly monitor human motion in various activities.

This paper presents a wireless electro-optic headstage for parallel electrophysiological recording of neural action potential (AP), local field potentials (LFP) and electromyography (EMG), while performing multichannel optical stimulation. The headstage is designed for enabling experiments with freely moving rodents, like small laboratory mice. It can record electrophysiological activity on up to 32 channels and stimulate with up to 4 optical channels using only commercial off the shelf (COTS) components. An embedded digital field-programmable gate array (FPGA) is used to allow real time digital separation of AP and LFP waveform and the recording of EMG. It also performs data reduction through AP detection and compression allowing to collect and transmit data wirelessly from 32 parallel channels. The digital filters are optimized to reduce latency so the 32 channels can be time multiplexed on a single data path to decrease power consumption and optimize resource utilisation. Performing signal separation allows the compression of the AP and the decimation of the LFP/EMG. This reduces the amount of data to transmit by a factor of 7.77 (for a firing rate of 50 AP/second) and allow a higher channel count for both signals. The headstage only weights 3.8 g w/ a 100 mAh battery and 4.68 g with the plastic packaging and on/off switch.

This paper presents a fully wireless neuroscience platform for enabling uninterrupted optogenetic experiments with live laboratory rodents. The system includes a wireless power transmission (WPT) home-cage using a 4-coil resonant link, a motion tracking system, a multichannel optogenetic headstage and a base station. The WPT home-cage uses a new hybrid parallel power transmitter (TX) coil array and segmented multicoil resonators to achieve high power transmission efficiency (PTE) and deliver high power across distances as high as 20 cm. The multicoil power receiver (RX) uses a RX coil with a diameter of 1.0 cm and a resonator coil with a diameter of 1.5 cm. The WPT home-cage average power transfer efficiency is 29.4%, at a nominal distance of 7 cm, for a power carrier frequency of 13.56 MHz. It has maximum and minimum PTE of 50% and 12% along the Z axis, and can deliver a constant power of 74 mW to supply the miniature neural headstage. The neural headstage includes 1 optical stimulation channel and 4 recording channels. We show that the hybrid WPT home-cage can properly power up the headstage without interruption, while the motion tracking system can track the activity of the animal in real time for enabling simultaneous behavioural and physiological assessment.

This paper presents different miniature wireless brain computer interfaces (BCI) enabling synchronized optogenetics and electrophysiology recording for various experimental conditions. These devices, which are entirely built using commercial off-the-shelf components, are validated in-vivo with small transgenic mice. First, a system including 32 electrophysiological recording channels and up to 32 high-power optical stimulation channels is presented. It can process 32 neuronal signals in parallel with high compression ratio using an embedded digital field-programmable gate array (FPGA) signal processor performing spike detection and data compression in-situ. Then, an advanced version featuring equivalent characteristics, but having the third of the size and the weight is presented. The design of a third system dedicated to small freely moving animals is presented. It includes 4 electrophysiological recording channels and 1 high-power optical stimulation channel. In-vivo result obtained in freely moving mice with this system are reported. This latter system performs aggressive data reduction using a resource optimized spike detector running on a low-power microcontroller unit. Finally, a multichannel optical stimulator operating within a network of up to 6 active devices is presented for enabling optogenetic behavioral experiments with several mice at once. These different wireless BCI all provide a multimodal access to brain activity through optogenetics and large-scale electrophysiology.

The demonstration will presents a battery powered multichannel wireless optogenetic headstage providing neural recording and optical stimulation capabilities simultaneously. The proposed headstage, which is entirely built using commercial off-the-shelf components, includes 32 electrophysiological recording channels and up to 32 high-power optical stimulation channels. It can process 32 neuronal signals in real-time with high compression ratio using an FPGA performing spike detection and data compression in-situ for fitting 32 channels over a low-power 2.4-GHz ISM data link (compression ratio > 500), hence greatly decreasing power and complexity. The presented headstage is small and lightweight enough for enabling optogenetic in-vivo experiments with freely moving transgenic rodents.

This paper presents a new wireless device for performing combined heart optogenetics and electrocardiography (ECG) recording. While optogenetics is extensively used in brain research, the designed prototype aims at expanding this ground-breaking experimental approach to research and applications on heart diseases. Such a system has a multitude of applications ranging from laboratory research to the development of non-invasive optogenetic pacemaker with ECG feedback for heart resynchronization. The proposed prototype is designed for experimental research with small freely moving animals enabling photo-stimulation of genetically modified cells using three different wavelengths (470, 615 and 625 nm) with four intensities by varying LED driving currents between 79 and 198 mA for a maximum optical power of 208.5 mW. The system can record the heart ECG through four electrodes positioned as specified by the Einthoven's triangle. It is controlled wirelessly through a host computer where the recorded data can be displayed in real-time. The device weights 1.12 g without battery and has an autonomy of 3 hours of continuous stimulation and recording using a 100-mAh battery. Results from in-vivo trials with laboratory mice show that the system can successfully record ECG.

Assistive technology (AT) tools and appliances are being more and more widely used and developed worldwide to improve the autonomy of people living with disabilities and ease the interaction with their environment. This paper describes an intuitive and wireless surface electromyography (sEMG) based body-machine interface for AT tools. Spinal cord injuries at C5-C8 levels affect patients' arms, forearms, hands, and fingers control. Thus, using classical AT control interfaces (keypads, joysticks, etc.) is often difficult or impossible. The proposed system reads the AT users' residual functional capacities through their sEMG activity, and converts them into appropriate commands using a threshold-based control algorithm. It has proven to be suitable as a control alternative for assistive devices and has been tested with the JACO arm, an articulated assistive device of which the vocation is to help people living with upper-body disabilities in their daily life activities. The wireless prototype, the architecture of which is based on a 3-channel sEMG measurement system and a 915-MHz wireless transceiver built around a low-power microcontroller, uses low-cost off-the-shelf commercial components. The embedded controller is compared with JACO's regular joystick-based interface, using combinations of forearm, pectoral, masseter, and trapeze muscles. The measured index of performance values is 0.88, 0.51, and 0.41 bits/s, respectively, for correlation coefficients with the Fitt's model of 0.75, 0.85, and 0.67. These results demonstrate that the proposed controller offers an attractive alternative to conventional interfaces, such as joystick devices, for upper-body disabled people using ATs such as JACO.

This paper presents the in vivo performances of a resource-optimized digital action potential (AP) detector featuring an adaptive threshold based on a new Sigma-delta control loop. The proposed AP detector is optimized for utilizing low hardware resources, which makes it suitable for real-time implementation on most common low-power microcontroller units (MCU). The adaptive threshold is calculated using a digital control loop based on a Sigma-delta modulator that precisely estimates the standard deviation of the neuronal signal amplitude. The detector was demonstrated using a common MCU from MSP430 family, incorporated into a small wireless platform for combined optogenetics and neura recording. The system has been fully characterized experimentally within in vivo experiments on a freely-moving transgenic mouse expressing ChannelRhodospin (Thy1::ChR2-YFP line4. The results demonstrate that the proposed AP detector can be used to achieve overall data reduction ratios above 11 hen transmitting only the detected APs. A comparison of the obtained results with other thresholding approaches shows that the pr posed detector provides similar performances to those significantly more resource demanding approaches.

Spike detection is a signal processing technique that can enable significant data rate reduction and resource savings in wireless brain monitoring. In these systems, energy-efficient spike detection algorithms are sought for enabling realtime signal processing while consuming low-power. As several spike detectors are based on ASIC, FPGA or low-power microcontroller unit (MCU), such algorithms must add little overhead to the entire system, while ensuring low error rate. In this paper, we present a comparative study of three different spike detection algorithms targeted toward implementation into low-power resource-constrained electronic systems. As practical validation, all candidate algorithms have been implemented on a popular low-power MCU and were fully characterized experimentally using previously recorded neural signals with different signal-to-noise ratios. A cost function based on detection rates, execution times, power consumption and resource utilization have been created and employed for comparing the detectors. The performances of all candidates are reported, and the best detector is identified. All candidate detectors present detection rate above 95% at high SNR, and above 78% for low SNR and can reduce the power consumption by up to 22.7%. This paper is the first to demonstrate the performances and hardware limitations of spike detectors on a low-power MCU system.

In this paper, we present a light-weight, wireless optogenetic headstage which provides optical neural stimulation and electrophysiological recording alongside on-the-fly neural signal processing. The proposed headstage is suitable to conduct long terms in-vivo experiments with small freely moving transgenic rodents, and features two implantable LED-coupled optical fibers and two electrophysiological recording channels while being powered by a small Lithium-ion battery. The headstage can transmit the raw neuronal signals or only spike waveforms after applying on-the-fly spike detection, which reduces power consumption by up to 14.5%. The headstage is entirely built using commercial off-the-shelf components, and the miniature design, using rigid-flex PCBs, results into a lightweight (7.4g) and compact device (25×20×15 mm). Low-power consumption is achieved by using on-the-fly spike detection alongside a real-time operating system which brings the headstage autonomy to 3h25 in full operation, including high-output power optical stimulation, micro-volts neuronal signal amplification and wireless transmission of the acquired waveforms.