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Bio-impedance Spectroscopy (BIS) is a technique that allows tissue analysis to diagnose a variety of diseases, such as medical imaging, cancer diagnosis, muscle fatigue detection, glucose measurement, and others under research. The development of CMOS integrated circuit front-ends for bioimpedance analysis is required by the increasing use of wearable devices in the healthcare field, as they offer key features for battery-powered wearable devices. These features include high miniaturization, low power consumption, and low voltage power supply. A key circuit in BIS systems is the current source, and one of the most common topology is the Enhanced Howland Current Source (EHCS). EHCS is also used when the current driver is driven by a pseudo-random signal like discrete interval binary sequences (DIBS), which, due to its broadband nature, requires high performance operational amplifiers. These facts lead to the need for a current source more compatible with DIBS signals, ultra-low power supply, standard CMOS integrated circuit, output current amplitude independent of input voltage amplitude, high output impedance, high load capability, high output voltage swing, and the possibility of tetra-polar BIS analysis, that is a pseudotetra-polar in the case of EHCS. The objective of this work is to evaluate the performance of the Switching CMOS Current Source (SCMOSCS) over EHCS using a Cole-skin model as a load using SPICE simulations (DC and AC sweeps and transient analysis). The SCMOSCS demonstrated an output impedance of more than 20 MΩ, a ± 2.5 V output voltage swing from a +3.3 V supply, a 275 μA current consumption, and a 10 kΩ load capacity. These results contrast with the + 1.5 V output voltage swing, the 3 kΩ load capacity, and the 4.9 mA current of the EHCS case.

This paper presents the behavioral modeling of three circuit architectures, which are suitable for heartbeat detection in wireless healthcare applications. The developed front-end models implement an analog system that detects the heartbeats for lower power consumption and higher detection accuracy without digital signal processing. Therefore, the circuit blocks and their critical specifications specifically regarding the noise and bandwidth are evaluated. The architectures are implemented in Verilog-A and their behavior simulations use real electrocardiogram (ECG) signals with different characteristics and conditions. All architectures have amplification, band-pass and notch response blocks as their first three stages. Then, the first circuit architecture detects the heartbeat using an adaptive threshold based on a pulse width approach. The second heartbeat detection scheme finds the maximum and minimum values of each beat using a sample-and-hold-based peak detector. The third model uses a similar peak detection scheme, but delays the analog signal for better precision. The simulation results show that all models can detect heartbeats when the input noise is less than $20 \mu V$ , the third having the highest noise tolerance of around $50 \mu V$ . For the ECG signal to maintain its characteristics with respect to its positive and negative peak, the bandpass response should present a low cutoff frequency below 10 Hz.

The past century has seen the ongoing development of amplifiers for different electrophysiological signals to study the work of the heart. Since the vacuum tube era, engineers and designers of bioamplifiers for recording electrophysiological signals have been trying to achieve similar objectives: increasing the input impedance and common-mode rejection ratio, as well as reducing power consumption and the size of the bioamplifier. This review traces the evolution of bioamplifiers, starting from circuits on vacuum tubes and discrete transistors through circuits on operational and instrumental amplifiers, and to combined analog-digital solutions on analog front-end integrated circuits. Examples of circuits and their technical features are provided for each stage of the bioamplifier development. Special emphasis is placed on the review of modern analog front-end solutions for biopotential registration, including their generalized structural diagram and table of comparative characteristics. A detailed review of analog front-end circuit integration in various practical applications is provided, with examples of the latest achievements in the field of electrocardiogram, electroencephalogram, and electromyogram registration. The review concludes with key points and insights for the future development of the analog front-end concept applied to bioelectric signal registration. [ABSTRACT FROM AUTHOR]

Two brain signal acquisition (BSA) front-ends incorporating two CMOS ultralow power, low-noise amplifier arrays and serializers operating in mosfet weak inversion region are presented. To boost the amplifier's gain for a given current budget, cross-coupled-pair active load topology is used in the first stages of these two amplifiers. These two BSA front-ends are fabricated in 130 and 180 nm CMOS processes, occupying 5.45 mm 2 and 0.352 mm 2 of die areas, respectively (excluding pad rings). The CMOS 130-nm amplifier array is comprised of 64 elements, where each amplifier element consumes 0.216 μW from 0.4 V supply, has input-referred noise voltage (IRNoise) of 2.19 μV[Formula: see text] corresponding to a power efficiency factor (PEF) of 11.7, and occupies 0.044 mm 2 of die area. The CMOS 180 nm amplifier array employs 4 elements, where each element consumes 0.69 μW from 0.6 V supply with IRNoise of 2.3 μV[Formula: see text] (corresponding to a PEF of 31.3) and 0.051 mm 2 of die area. Noninvasive electroencephalographic and invasive electrocorticographic signals were recorded real time directly on able-bodied human subjects, showing feasibility of using these analog front-ends for future fully implantable BSA and brain- computer interface systems.

Recent years have seen fast advances in neural recording circuits and systems as they offer a promising way to investigate real-time brain monitoring and the closed-loop modulation of psychological disorders and neurodegenerative diseases. In this context, this tutorial brief presents a concise overview of concepts and design methodologies of neural recording, highlighting neural signal characteristics, system-level specifications and architectures, circuit-level implementation, and noise reduction techniques. Future trends and challenges of neural recording are finally discussed. [ABSTRACT FROM AUTHOR]

A 5–30 Gb/s receiver analog front-end (AFE) cascading transimpedance amplifier (TIA) and continuous-time linear equalizer (CTLE) were implemented using a Taiwan Semiconductor 180 nm process. The system comprises a two-stage differential input pair CTLE, TIA, and a differential termination resistor Rm. A source-degenerated transconductance stage was adopted in the CTLE, and source follower and shunt feedback resistor stages were adopted in the TIA. The proposed CTLE could achieve high frequencies by altering the tail current with fixed degenerate capacitance CS and resistance RS. The proposed AFE achieved high bandwidth, and the use of a feedback resistor Rf and inductor Lf improved its high-frequency performance. Simulation results revealed that the CTLE can compensate for 16 dB of channel loss at a 3 GHz Nyquist frequency and can open closed eyes in a 6 Gb/s non-return-to-zero signal with a bit error rate of 0.16 × 10−12 for a 231 − 1 pseudorandom binary sequence input. The AFE could compensate for 12 dB of channel loss at a 15 GHz Nyquist frequency and can open closed eyes in a 30 Gb/s PAM4 signal from a pseudorandom binary sequence input; it consumed 27 mW of power at 1.8 V. [ABSTRACT FROM AUTHOR]

In this paper, low-noise incremental delta–sigma analog front end (AFE) integrated circuit (IC) for capacitive microsensors is presented. A conventional capacitance-to-digital converter (CDC) mainly uses a multi-stage capacitive sensing amplified stage (CSA) and analog-to-digital converters. The multi-stage CSA is not suitable for application in various Internet of things (IoT) devices that require low power because the power consumption of the analog front-end circuit increases in proportion to the number of amplifiers and the chip area increases. So, the presented delta-sigma AFE can convert the capacitance changes to the digital codes directly. This structure can achieve a small active area and low power consumption. The delta–sigma AFE achieves low-noise and high linearity using a capacitor polarity swapping technique. The measured effective resolution is 16.2 bits, and the non-linearity is 0.05% full-scale output (FSO). The integrated circuit is implemented in a 0.18-µm standard CMOS process. All functional blocks, including the analog circuits (bandgap reference, voltage reference, and delta–sigma capacitance-to-digital converter) and digital block (accumulator and timing generator), are integrated on a chip. The proposed incremental delta–sigma AFE consumes 1.12 mW of power from a 3.3-V supply at a sampling frequency of 500 kHz and occupies a total active area of 0.42 mm2. [ABSTRACT FROM AUTHOR]

This paper proposes a high frame rate analog front-end (AFE) IC combined with a piezoelectric micromachined ultrasonic transducer (PMUT) for ultrasound imaging systems (UISs). The proposed AFE IC, which consists of 8-channel transceivers and a peripheral circuit, employs analog multi-line acquisition (AMLA) based on sub-array beamforming. This AMLA generates multiple scanlines during a scan time by simultaneously and precisely controlling the delay of the AFE IC, thus achieving the high frame rate of the AFE IC. The sensitivities of the PMUT for transmitting and receiving ultrasound waves are simulated to be 67.5 kPa/V and 1.2 mV/kPa, respectively, which are high enough to mitigate the needs of the high-voltage electronics for UISs. The proposed AFE IC was fabricated using a 0.18- $\mu \text{m}$ silicon-on-insulator process and occupies an active area of 2.56 mm $\times1.68$ mm. The measurement results using the simulated PMUT parameters show that the proposed AFE IC with the AMLA precisely controls its delay up to $1~\mu \text{s}$ at a clock frequency of 40 MHz. It also acquires more than 9,000 scan/s even at a focal depth of 150 mm, which is at least twice greater than those of prior works, achieving a high frame rate of 93 Hz when 100 scanlines are used for a frame. In addition, a figure-of-merit, which considers the energy consumption per channel/scan and the energy loss, is achieved to be 20 nJ/scan/dB at a focal depth of 150 mm and a center frequency of 3.6 MHz; this is the best result compared to previously reported works. Therefore, the proposed AFE IC is suitable for UISs requiring a high frame rate.

There has been considerable interest in electrical impedance tomography (EIT) to provide low-cost, radiation-free, real-time and wearable means for physiological status monitoring. To be competitive with other well-established imaging modalities, it is important to understand the requirements of the specific application and determine a suitable system design. This paper presents an overview of EIT circuits and systems including architectures, current drivers, analog front-end and demodulation circuits, with emphasis on integrated circuit implementations. Commonly used circuit topologies are detailed, and tradeoffs are discussed to aid in choosing an appropriate design based on the application and system priorities. The paper also describes a number of integrated EIT systems for biomedical applications, as well as discussing current challenges and possible future directions.

This paper presents a high-resolution analog front-end (AFE) circuit operating in the 25 kHz signal bandwidth. The AFE consists of a capacitively-coupled chopper instrumentation amplifier (CCIA) and a third-order delta-sigma modulator (DSM). Signal distortion often occurs at the junction of CCIA and DSM, so the resistor-based discrete-time DSM (RB DT–DSM) is proposed to solve this problem. Both conventional DT–DSM and the proposed RB DT–DSM are designed and compared through simulations and chip measurements. When a conventional DT–DSM is used, a switched-capacitor integrator with a large capacitor and switch are connected to the CCIA output node, so large switching noise, significant charge injection, and a long settling time occur at the CCIA output, resulting in harmonic distortions. Therefore, CCIA requires more power consumption to reduce the settling time for conventional DT–DSM architectures. On the other hand, when the proposed RB DT–DSM is used, only a resistor and a small switch are connected to the CCIA output. Due to this novel interface technique, the switching noise is very small at the CCIA output, and the settling of the CCIA becomes fast. As a result, it is possible to effectively eliminate harmonic distortions while reducing the power requirement of the CCIA. Both the conventional and proposed AFE circuits were manufactured by a 0.18- $\mu \text{m}$ complementary metal-oxide-semiconductor (CMOS) process. The conventional AFE shows a peak SNR of 101.1 dB and a peak SNDR of 91.2 dB. The proposed AFE with RB DT–DSM shows a peak SNR of 101.3 dB and a peak SNDR of 100.5 dB, which verifies the significant reduction in harmonic distortions.

This paper discusses a 56-Gb/s PAM4 receiver analog-front end (AFE) implemented in TSMC 40-nm CMOS process. The system consists of a differential 100-Ω termination, a two-stage continuous-time linear equalizer (CTLE), a variable gain amplifier (VGA), and an output buffer. The source-degenerated transconductance stage and inverter-based transimpedance (TIA) with source follower structure are adopted for both CTLE and VGA. The utilization of source follower can solve the harsh DC operation problem in conventional inverter-based TIA and extend the bandwidth. By altering the source-degenerated resistors and capacitors in the two-stage CTLE, the proposed AFE can reach fixed peaking frequency with 9-dB compensation range at high frequency. Moreover, it can also achieve a fixed bandwidth at 14 GHz and 9.5-dB DC gain tuning range when altering the feedback resistors and negative capacitance compensation network (NCC) in VGA. Measurement results demonstrate that: it can compensate for 7.3-dB channel loss at 14-GHz Nyquist frequency and open the closed eye for 56-Gb/s PAM4 signal with BER < 10−8 from 215 – 1 PRBS input. The core of the AFE occupies 0.32-mm2 area and consumes 30-mW power from 1.1/1.2-V supply. [ABSTRACT FROM AUTHOR]

Abstract Background It is widely accepted by the scientific community that bioelectrical signals, which can be used for the identification of neurophysiological biomarkers indicative of a diseased or pathological state, could direct patient treatment towards more effective therapeutic strategies. However, the design and realisation of an instrument that can precisely record weak bioelectrical signals in the presence of strong interference stemming from a noisy clinical environment is one of the most difficult challenges associated with the strategy of monitoring bioelectrical signals for diagnostic purposes. Moreover, since patients often have to cope with the problem of limited mobility being connected to bulky and mains-powered instruments, there is a growing demand for small-sized, high-performance and ambulatory biopotential acquisition systems in the Intensive Care Unit (ICU) and in High-dependency wards. Finally, to the best of our knowledge, there are no commercial, small, battery-powered, wearable and wireless recording-only instruments that claim the capability of recording electrocorticographic (ECoG) signals. Methods To address this problem, we designed and developed a low-noise (8 nV/√Hz), eight-channel, battery-powered, wearable and wireless instrument (55 × 80 mm2). The performance of the realised instrument was assessed by conducting both ex vivo and in vivo experiments. Results To provide ex vivo proof-of-function, a wide variety of high-quality bioelectrical signal recordings are reported, including electroencephalographic (EEG), electromyographic (EMG), electrocardiographic (ECG), acceleration signals, and muscle fasciculations. Low-noise in vivo recordings of weak local field potentials (LFPs), which were wirelessly acquired in real time using segmented deep brain stimulation (DBS) electrodes implanted in the thalamus of a non-human primate, are also presented. Conclusions The combination of desirable features and capabilities of this instrument, namely its small size (~one business card), its enhanced recording capabilities, its increased processing capabilities, its manufacturability (since it was designed using discrete off-the-shelf components), the wide bandwidth it offers (0.5–500 Hz) and the plurality of bioelectrical signals it can precisely record, render it a versatile and reliable tool to be utilized in a wide range of applications and environments.

In this paper, we propose a structure of a portable cardiograph. Contents and functions of all blocks are overviewed. We describe two main structures of portable cardiographs, prepare a comparative table of characteristics for different analog front-ends. All individual features of the chips are listed as well. We register a test signal and try to improve its quality by means of digital filtration, taking the signal gained from certified Biopack equipment as a reference. We calculate device power consumption to compute required battery volume. Finally, energy efficient preprocessing algorithm is proposed.

In the application of AI speech interaction, the analog front-end(AFE) circuit in audio SoC is responsible for digitalization of microphone analog output, and it is a bridge between the analog signal and the digital processing unit. The AFE function and dynamic performance are greatly limited due to the influence of complex audio environment application, the device mismatch and other non-ideal factors. The audio SoC AFE is analyzed in this paper, and the circuit characteristics and current development is discussed in detail. Finally the research route for design challenge is presented and development trend is prospected.

Abstract Schumann resonance oscillation detection is a complex procedure which requires customized and high-quality measurement systems. The primary objective of this work was to design and implement a stand-alone, portable, and low-cost receiver able to measure as much Schumann resonance harmonics as possible. Design, as well as detailed analysis of the efficient induction coil magnetic antenna and the low-noise amplifying-filtering chain, is presented. The detection system includes two coils back to back, resulting in a total coil length of 60 cm. The filtering and amplification chain exhibits an experimentally measured total passband gain equal to 112 dB at 10 Hz and as low as 2.88 nV/√Hz equivalent input noise. In order to validate the new portable ELF Schumann resonance detection and monitoring system, we took measurements at various spots “relatively” free from man-made electromagnetic pollution. Results have shown very clear Schumann resonance peaks for the first six modes with 10-min acquisition time.

Aiming at the requirement of the hybrid sky-surface network for radar receiver, the design of analog front-end for dual-band multi-channel digital radar receiver based on software radio idea is proposed and implemented. The design takes an analog-to-digital conversion chip as the core, which achieves the amplification and sampling of 8-channel dual-band receive signals , ensures the consistency of the channel gain, and simplifies the circuit when keeping the flexibility. Finally, through the system simulation and field test of the receiver, the correctness of the design is proved, and it can meet the practical application requirements.

In this study, a fully integrated electroencephalogram/functional near-infrared spectroscopy (EEG/fNIRS) brain monitoring system was designed to fulfill the demand for a miniaturized, light-weight, low-power-consumption, and low-cost brain monitoring system as a potential tool with which to screen for brain diseases. The system is based on the ADS1298IPAG Analog Front-End (AFE) and can simultaneously acquire two-channel EEG signals with a sampling rate of 250 SPS and six-channel fNIRS signals with a sampling rate of 8 SPS. AFE is controlled by Teensy 3.2 and powered by a lithium polymer battery connected to two protection circuits and regulators. The acquired EEG and fNIRS signals are monitored and stored using a Graphical User Interface (GUI). The system was evaluated by implementing several tests to verify its ability to simultaneously acquire EEG and fNIRS signals. The implemented system can acquire EEG and fNIRS signals with a CMRR of −115 dB, power consumption of 0.75 mW/ch, system weight of 70.5 g, probe weight of 3.1 g, and a total cost of USD 130. The results proved that this system can be qualified as a low-cost, light-weight, low-power-consumption, and fully integrated EEG/fNIRS brain monitoring system.

Background: It is widely accepted by the scientific community that bioelectrical signals, which can be used for the identification of neurophysiological biomarkers indicative of a diseased or pathological state, could direct patient treatment towards more effective therapeutic strategies. However, the design and realisation of an instrument that can precisely record weak bioelectrical signals in the presence of strong interference stemming from a noisy clinical environment is one of the most difficult challenges associated with the strategy of monitoring bioelectrical signals for diagnostic purposes. Moreover, since patients often have to cope with the problem of limited mobility being connected to bulky and mains-powered instruments, there is a growing demand for small-sized, high-performance and ambulatory biopotential acquisition systems in the Intensive Care Unit (ICU) and in High-dependency wards. Finally, to the best of our knowledge, there are no commercial, small, battery-powered, wearable and wireless recording-only instruments that claim the capability of recording electrocorticographic (ECoG) signals.Methods: To address this problem, we designed and developed a low-noise (8 nV/√Hz), eight-channel, battery-powered, wearable and wireless instrument (55 × 80 mm2). The performance of the realised instrument was assessed by conducting both ex vivo and in vivo experiments.Results: To provide ex vivo proof-of-function, a wide variety of high-quality bioelectrical signal recordings are reported, including electroencephalographic (EEG), electromyographic (EMG), electrocardiographic (ECG), acceleration signals, and muscle fasciculations. Low-noise in vivo recordings of weak local field potentials (LFPs), which were wirelessly acquired in real time using segmented deep brain stimulation (DBS) electrodes implanted in the thalamus of a non-human primate, are also presented.Conclusions: The combination of desirable features and capabilities of this instrument, namely its small size (~one business card), its enhanced recording capabilities, its increased processing capabilities, its manufacturability (since it was designed using discrete off-the-shelf components), the wide bandwidth it offers (0.5-500 Hz) and the plurality of bioelectrical signals it can precisely record, render it a versatile and reliable tool to be utilized in a wide range of applications and environments. [ABSTRACT FROM AUTHOR]

This brief presents an analog front-end (AFE) specially developed for a water conductivity measurement system for water network management applications. Water conductivity is one of the best ways to detect a problem with the water in the network. In order to trigger an alarm it is only required to detect a 10% variation, which allows to simplify the system design. The proposed sensor uses four platinum micro-machined electrodes, where a 6 kHz signal is applied to the outer electrodes and a voltage signal is read in the inner ones. A programmable switched-capacitor (SC) bandpass filter is included in the analog-to-digital (A/D) channel to attenuate the interference from the 50 Hz power network, that shares the same environment with the water network, and to allow using a square wave signal to drive the sensor, thus simplifying the overall system. The resulting sine wave is digitized using a passive continuous-time (CT) 2nd order $\Delta\Sigma$ modulator. The output bitstream is decimated, using a sinc filter, and the RMS value of the voltage is calculated. The A/D acquisition channel was implemented in a 130 nm CMOS technology, using a supply voltage of 0.9 V and a clock frequency of 1 MHz (SC amplifier and SC bandpass filter) and 4 MHz ($\Delta \Sigma $ modulator). Measurement results show a SNDR of 44.4 dB for an input signal of 6 kHz and an amplitude of −41 dBV. The channel dissipates 386 $\mu \text{W}$ and occupies an area of 0.27 mm2. [ABSTRACT FROM AUTHOR]

For low-power hearing aid device application, a fully integrated optimized hearing aid SoC structure is proposed in this paper. The SoC consists of high-resolution, low-power analog front-end (AFE), time-division-multiplexed power-on-reset circuit, charge pump, digital signal processing (DSP) platform, and Class-D amplifier. A novel peak-statistical algorithm is proposed to track signal amplitude and adjust automatic gain control loop gain precisely. A comparative DWA is applied to break the correlation of in-band tone and sequential selection scheme, which realizes second-order noise shaping and suppresses harmonic effectively. The SoC has been implemented with 0.13 µm CMOS process. By measurement, it shows that the peak signal-to-noise ratio (SNR) of AFE is 82.6 dB and peak SNR of Class-D amplifier is 79.8 dB. Also, three main algorithms of wide dynamic range compression, noise reduction, and feedback cancelation are executed through DSP platform. With 1 V supply voltage, total SoC power is 1.1 mW and core area is 9.3 mm2. Based on our SoC, a hearing aid device prototype is produced that shows its great potential for mass manufacture in the future. [ABSTRACT FROM AUTHOR]

This paper presents an integrated switched-capacitor-based low-power fully-differential analog front-end (AFE) for simultaneously detecting electrocardiogram (ECG) and respiration rate (RR). A current-driven passive mixer is used to up-convert the RR signal, and a preamplifier including a driven-ground common-mode feedback (CMFB) loop amplifies both the ECG and modulated RR signals. The modulated RR signal is then down-converted to baseband to be further amplified and detected. Switched capacitors (SCs) are widely used in the proposed AFE to provide large time constants that are also easily adjustable via the clock frequency. The chip is highly programmable, which enables complex automatic gain control (AGC) algorithms based on using a microcontroller within the loop. The AFE was fabricated in the OnSemi 0.5 μ m CMOS process. Both simulation and measurement results verify the functionality of the chip for measuring multiple biosignals, including ECG, RR, electromyography (EMG), and galvanic skin response (GSR). Performance metrics are competitive compared to the state of the art. [ABSTRACT FROM AUTHOR]

Analog Front-End (AFE) is a part of physical layer (PHY) at Visible Light Communications (VLC) system which has important things to transmit and receive the data. This research is a preliminary study in designing AFE transceiver which consists of LED driver and Trans-impedance Amplifier (TIA). The results are used as advanced reference design of AFE system and also to determine the modulations range (analog as well as digital). The design verification is based on data transmission which generated by signal generator. Square wave signal is used as digital modulation representative, and sinusoid signal as analog modulation representatitive. The range of VLC channels is about 50 cm with 0o angle. The result shows that digital modulation bandwidth is about 1 KHz to 10 KHz frequency, and analog modulation bandwidth is from 200 KHz to 600 KHz. We also find a significant impact by adding 1 pF capacitor to reduce noise gain. Furthermore, color filter testing is performed to investigate the design performance.

The recently growing progress in neuroscience research and relevant achievements, as well as advancements in the fabrication process, have increased the demand for neural interfacing systems. Brain−machine interfaces (BMIs) have been revealed to be a promising method for the diagnosis and treatment of neurological disorders and the restoration of sensory and motor function. Neural recording implants, as a part of BMI, are capable of capturing brain signals, and amplifying, digitizing, and transferring them outside of the body with a transmitter. The main challenges of designing such implants are minimizing power consumption and the silicon area. In this paper, multi-channel neural recording implants are surveyed. After presenting various neural-signal features, we investigate main available neural recording circuit and system architectures. The fundamental blocks of available architectures, such as neural amplifiers, analog to digital converters (ADCs) and compression blocks, are explored. We cover the various topologies of neural amplifiers, provide a comparison, and probe their design challenges. To achieve a relatively high SNR at the output of the neural amplifier, noise reduction techniques are discussed. Also, to transfer neural signals outside of the body, they are digitized using data converters, then in most cases, the data compression is applied to mitigate power consumption. We present the various dedicated ADC structures, as well as an overview of main data compression methods.

The increased popularity of brain-computer interfaces (BCIs) has created a new demand for miniaturized and low-cost electroencephalogram (EEG) acquisition devices for entertainment, rehabilitation, and scientific needs. The lack of scientific analysis for such system design, modularity, and unified validation tends to suppress progress in this field and limit supply for new low-cost device availability. To eliminate this problem, this paper presents the design and evaluation of a compact, modular, battery powered, conventional EEG signal acquisition board based on an ADS1298 analog front-end chip. The introduction of this novel, vertically stackable board allows the EEG scaling problem to be solved by effectively reconfiguring hardware for small or more demanding applications. The ability to capture 16 to 64 EEG channels at sample rates from 250 Hz to 1000 Hz and to transfer raw EEG signal over a Bluetooth orWi-Fi interface was implemented. Furthermore, simple but effective assessment techniques were used for system evaluation. While conducted tests confirm the validity of the system against official datasheet specifications and for real-world applications, the proposed quality verification methods can be further employed for analyzing other similar EEG devices in the future. With 6.59 microvolts peak-to-peak input referred noise and a –97 dB common mode rejection ratio in 0–70 Hz band, the proposed design can be qualified as a low-cost precision cEEG research device. [ABSTRACT FROM AUTHOR]

This brief presents a high-density, low-noise analog front-end (AFE) for capacitively coupled neural recording applications. Conventional capacitively coupled AFEs, when chopper-stabilized, require large coupling capacitors or servo loops to minimize 1/f^2 input-referred noise and chopper-induced offsets, limiting channel density. In this brief, a current-mode capacitively coupled chopper instrumentation amplifier with embedded delta-sigma analog-to-digital converter (ADC) is presented that enables an area-efficient low-noise design via chopper-stabilized current-mode amplification. In this design, 60 channels are implemented in a 2 \times 2 mm2 180-nm CMOS chip, and each channel consumes 4~ \mu \textW , achieves an input referred noise of 160 nV/ \sqrt \textHz , and an ADC effective number of bits of 8.5 bits. [ABSTRACT FROM PUBLISHER]

When designing an analog front-end for neural interfacing, it is hard to evaluate the interplay of priority features that one must upkeep. Given the competing nature of design requirements for such systems a good understanding of these trade-offs is necessary. Low power, chip size, noise control, gain, temporal resolution and safety are the salient ones. There is a need to expose theses critical features for high performance neural amplifiers as the density and performance needs of these systems increases. This review revisits the basic science behind the engineering problem of extracting neural signal from living tissue. A summary of architectures and topologies is then presented and illustrated through a rich set of examples based on the literature. A survey of existing systems is presented for comparison based on prevailing performance metrics.