High Precision Ping-pong Auto-zeroed Lock-in Fluorescence Photometry Sensor.

This paper presents a high-precision CMOS fluorescence photometry sensor using a novel lock-in amplification scheme based on switched-biasing and ping-pong auto-zeroing techniques. The CMOS sensor includes two photodiodes and a lock-in amplifier (LIA) operating at 1 kHz. The LIA comprises a differential low-noise amplifier using a novel switched-biasing ping-pong auto-zeroed scheme, an automatic phase aligner, a programmable gain amplifier, a band-pass filter, a mixer, and an output low-pass filter. The design is fabricated in 0.18-µm CMOS process, and the measurement shows that the LIA can retrieve noisy input signals with a dynamic reserve of 42 dB, while consuming only 0.7 mW from a 1.8 V supply voltage. The measured results show that the LIA can detect a wide range of incident light power from 8 nW to 24 µW. The proposed design is encapsulated in a 3D-printed housing allowing for real-time in vitro biomarker detection. This ambulatory platform uses an LED and a fiber optic to convey the excitation light to the sample and retrieve the fluorescence signal. Experiments with a beads solution diluted in PBS demonstrate that the sensor has a sensitivity of 1:100 k. Experimental results obtained in vitro with NIH3T3 mouse cells tagged with membrane dye show the ability of the prototype to detect different densities of cell culture. The portable prototype, which includes optical filters and a small 30 mm × 36 mm × 30 mm printed circuit board enclosed inside the 3D-printed housing, consumes 36.7 mW and weighs 120 g.

A Low-Power High Input Range PPG Readout Amplifier with a Current Buffer Input.

This paper presents ultra-low power photoplethysmography (PPG) readout circuits. The proposed system architecture uses a current buffer between the photodiode (PD) and the transimpedance amplifier (TIA) to isolate the large parasitic capacitance of the PD leading to improves the power consumption of the TIA. A class AB topology is exploited at the output of the amplifier, which allows for increased drive capability without the use of auxiliary circuits. The maximum input current range of the TIA is 160 µA, so the large DC current of the input signal does not saturate the circuit. In the LED driver circuit, by varying the duty cycle of a pulse wave modulation (PWM) signal, the ON and OFF times of the circuits. The amplifier and LED driver are manufactured in the 130 nm TSMC CMOS process. The power consumption of the circuits with a duty cycle of 1% is 3.28 µW (at VDD = 1.2V).Clinical Relevance- Vital signs are becoming a very important research topic due to the recent prevalence of COVID-19 and other respiratory diseases. This research aims to develop and interface circuits to monitor vital signs including blood pressure, heart rate, and respiratory rate to study respiratory disease, drug safety, and efficacy.

Recent Advances in CMOS Electrochemical Biosensor Design for Microbial Monitoring: Review and Design Methodology.

Rapid, high-sensitivity, and real-time characterization of microorganisms plays a significant role in several areas, including clinical diagnosis, human healthcare, early detection of outbreaks, and the protection of living beings. Integrating microbiology and electrical engineering promises the development of low-cost, miniaturized, autonomous, and high-sensitivity sensors to quantify and characterize bacterial strains at various concentrations. Electrochemical-based biosensors are receiving particular attention in microbiological applications among the different biosensing devices. Several approaches have been adopted to design and fabricate cutting-edge, miniaturized, and portable electrochemical biosensors to track and monitor bacterial cultures in real time. These techniques differ in their sensing interface circuits and microelectrode fabrication. The goals of this review are (1) to summarize the current state of CMOS sensing circuit designs in label-free electrochemical biosensors for bacteria monitoring and (2) to discuss the material and size of the electrodes used in electrochemical biosensors in microbiological applications. In this paper, we reviewed the latest and most advanced CMOS integrated interface circuits that have recently been used in electrochemical biosensors to identify and characterize bacteria species, such as impedance spectroscopy, capacitive, amperometry, and voltammetry, etc. In addition to the interface circuit design, other crucial factors, such as the material and scale of the electrodes, must be considered to increase the sensitivity of electrochemical biosensors. Surveying the literature in this field improves our knowledge about the impact of electrode designs and materials on sensing precision and will help future designers adapt, design, and fabricate appropriate electrode configurations based on their application. Thus, we summarized the conventional microelectrode designs and materials mainly employed in microbial sensors, including interdigitated electrodes (IDEs), microelectrode arrays (MEAs), paper, and carbon-based electrodes, etc.

A Wireless Electro-Optic Platform for Multimodal Electrophysiology and Optogenetics in Freely Moving Rodents.

This paper presents the design and the utilization of a wireless electro-optic platform to perform simultaneous multimodal electrophysiological recordings and optogenetic stimulation in freely moving rodents. The developed system can capture neural action potentials (AP), local field potentials (LFP) and electromyography (EMG) signals with up to 32 channels in parallel while providing four optical stimulation channels. The platform is using commercial off-the-shelf components (COTS) and a low-power digital field-programmable gate array (FPGA), to perform digital signal processing to digitally separate in real time the AP, LFP and EMG while performing signal detection and compression for mitigating wireless bandwidth and power consumption limitations. The different signal modalities collected on the 32 channels are time-multiplexed into a single data stream to decrease power consumption and optimize resource utilization. The data reduction strategy is based on signal processing and real-time data compression. Digital filtering, signal detection, and wavelet data compression are used inside the platform to separate the different electrophysiological signal modalities, namely the local field potentials (1-500 Hz), EMG (30-500 Hz), and the action potentials (300-5,000 Hz) and perform data reduction before transmitting the data. The platform achieves a measured data reduction ratio of 7.77 (for a firing rate of 50 AP/second) and weights 4.7 g with a 100-mAh battery, an on/off switch and a protective plastic enclosure. To validate the performance of the platform, we measured distinct electrophysiology signals and performed optogenetics stimulation in vivo in freely moving rondents. We recorded AP and LFP signals with the platform using a 16-microelectrode array implanted in the primary motor cortex of a Long Evans rat, both in anesthetized and freely moving conditions. EMG responses to optogenetic Channelrhodopsin-2 induced activation of motor cortex via optical fiber were also recorded in freely moving rodents.

A Transferable Adaptive Domain Adversarial Neural Network for Virtual Reality Augmented EMG-Based Gesture Recognition.

Within the field of electromyography-based (EMG) gesture recognition, disparities exist between the offline accuracy reported in the literature and the real-time usability of a classifier. This gap mainly stems from two factors: 1) The absence of a controller, making the data collected dissimilar to actual control. 2) The difficulty of including the four main dynamic factors (gesture intensity, limb position, electrode shift, and transient changes in the signal), as including their permutations drastically increases the amount of data to be recorded. Contrarily, online datasets are limited to the exact EMG-based controller used to record them, necessitating the recording of a new dataset for each control method or variant to be tested. Consequently, this paper proposes a new type of dataset to serve as an intermediate between offline and online datasets, by recording the data using a real-time experimental protocol. The protocol, performed in virtual reality, includes the four main dynamic factors and uses an EMG-independent controller to guide movements. This EMG-independent feedback ensures that the user is in-the-loop during recording, while enabling the resulting dynamic dataset to be used as an EMG-based benchmark. The dataset is comprised of 20 able-bodied participants completing three to four sessions over a period of 14 to 21 days. The ability of the dynamic dataset to serve as a benchmark is leveraged to evaluate the impact of different recalibration techniques for long-term (across-day) gesture recognition, including a novel algorithm, named TADANN. TADANN consistently and significantly ( [Formula: see text]) outperforms using fine-tuning as the recalibration technique.

A Wireless Electro-Optic Headstage With a 0.13- µm CMOS Custom Integrated DWT Neural Signal Decoder for Closed-Loop Optogenetics.

We present a wireless electro-optic headstage that uses a 0.13- µm CMOS custom integrated circuit (IC) implementing a digital neural decoder (ND-IC) for enabling real-time closed-loop (CL) optogenetics. The ND-IC processes the neural activity data using three digital cores: 1) the detector core detects and extracts the action potential (AP) of individual neurons by using an adaptive threshold; 2) the data compression core compresses the detected AP by using an efficient Symmlet-2 discrete wavelet transform (DWT) processor for decreasing the amount of data to be transmitted by the low-power wireless link; and 3) the classification core sorts the compressed AP into separated clusters on the fly according to their wave shapes. The ND-IC encompasses several innovations: 1) the compression core decreases the complexity from O(n 2) to O(n · log(n)) compared to the previous solutions, while using two times less memory, thanks to the use of a new coefficient sorting tree; and 2) the AP classification core reuses both the compressed DWT coefficients to perform implicit dimensionality reduction, which allows for performing intensive signal processing on-chip, while increasing power and hardware efficiency. This core also reuses the signal standard deviation already computed by the AP detector core as threshold for performing automatic AP sorting. The headstage also introduces innovations by enabling a new wireless CL scheme between the neural data acquisition module and the optical stimulator. Our CL scheme uses the AP sorting and timing information produced by the ND-IC for detecting complex firing patterns within the brain. The headstage is also smaller (1.13 cm 3), lighter (3.0 g with a 40 mAh battery) and less invasive than the previous solutions, while providing a measured autonomy of 2h40, with the ND-IC. The whole system and the ND-IC are first validated in vivo in the LD thalamus of a Long-Evans rat, and then in freely-moving CL experiments involving a mouse virally expressing ChR2-mCherry in inhibitory neurons of the prelimbic cortex, and the results show that our system works well within an in vivo experimental setting with a freely moving mouse.

A Low-Cost, Wireless, 3-D-Printed Custom Armband for sEMG Hand Gesture Recognition.

Wearable technology can be employed to elevate the abilities of humans to perform demanding and complex tasks more efficiently. Armbands capable of surface electromyography (sEMG) are attractive and noninvasive devices from which human intent can be derived by leveraging machine learning. However, the sEMG acquisition systems currently available tend to be prohibitively costly for personal use or sacrifice wearability or signal quality to be more affordable. This work introduces the 3DC Armband designed by the Biomedical Microsystems Laboratory in Laval University; a wireless, 10-channel, 1000 sps, dry-electrode, low-cost (∼150 USD) myoelectric armband that also includes a 9-axis inertial measurement unit. The proposed system is compared with the Myo Armband by Thalmic Labs, one of the most popular sEMG acquisition systems. The comparison is made by employing a new offline dataset featuring 22 able-bodied participants performing eleven hand/wrist gestures while wearing the two armbands simultaneously. The 3DC Armband systematically and significantly ( p < 0.05 ) outperforms the Myo Armband, with three different classifiers employing three different input modalities when using ten seconds or more of training data per gesture. This new dataset, alongside the source code, Altium project and 3-D models are made readily available for download within a Github repository.

A Wireless Headstage for Combined Optogenetics and Multichannel Electrophysiological Recording.

This paper presents a wireless headstage with real-time spike detection and data compression for combined optogenetics and multichannel electrophysiological recording. The proposed headstage, which is intended to perform both optical stimulation and electrophysiological recordings simultaneously in freely moving transgenic rodents, is entirely built with commercial off-the-shelf components, and includes 32 recording channels and 32 optical stimulation channels. It can detect, compress and transmit full action potential waveforms over 32 channels in parallel and in real time using an embedded digital signal processor based on a low-power field programmable gate array and a Microblaze microprocessor softcore. Such a processor implements a complete digital spike detector featuring a novel adaptive threshold based on a Sigma-delta control loop, and a wavelet data compression module using a new dynamic coefficient re-quantization technique achieving large compression ratios with higher signal quality. Simultaneous optical stimulation and recording have been performed in-vivo using an optrode featuring 8 microelectrodes and 1 implantable fiber coupled to a 465-nm LED, in the somatosensory cortex and the Hippocampus of a transgenic mouse expressing ChannelRhodospin (Thy1::ChR2-YFP line 4) under anesthetized conditions. Experimental results show that the proposed headstage can trigger neuron activity while collecting, detecting and compressing single cell microvolt amplitude activity from multiple channels in parallel while achieving overall compression ratios above 500. This is the first reported high-channel count wireless optogenetic device providing simultaneous optical stimulation and recording. Measured characteristics show that the proposed headstage can achieve up to 100% of true positive detection rate for signal-to-noise ratio (SNR) down to 15 dB, while achieving up to 97.28% at SNR as low as 5 dB. The implemented prototype features a lifespan of up to 105 minutes, and uses a lightweight (2.8 g) and compact [Formula: see text] rigid-flex printed circuit board.

Wireless sEMG-Based Body-Machine Interface for Assistive Technology Devices.

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.

A Wireless Optogenetic Headstage with Multichannel Electrophysiological Recording Capability.

We present a small and lightweight fully wireless optogenetic headstage capable of optical neural stimulation and electrophysiological recording. The headstage is suitable for conducting experiments with small transgenic rodents, and features two implantable fiber-coupled light-emitting diode (LED) and two electrophysiological recording channels. This system is powered by a small lithium-ion battery and is entirely built using low-cost commercial off-the-shelf components for better flexibility, reduced development time and lower cost. Light stimulation uses customizable stimulation patterns of varying frequency and duty cycle. The optical power that is sourced from the LED is delivered to target light-sensitive neurons using implantable optical fibers, which provide a measured optical power density of 70 mW/mm² at the tip. The headstage is using a novel foldable rigid-flex printed circuit board design, which results into a lightweight and compact device. Recording experiments performed in the cerebral cortex of transgenic ChR2 mice under anesthetized conditions show that the proposed headstage can trigger neuronal activity using optical stimulation, while recording microvolt amplitude electrophysiological signals.