Neural Amplifier Research Articles

Analysis of a Low-Power OTA-Based Neural Amplifier Design for EEG Signal Acquisition

This paper presents the design of an efficient operational transconductance amplifier (OTA) to be explicitly used for electroencephalogram (EEG) signal acquisition in neural amplifiers (NAs). The central objective of this study revolves around addressing the fundamental compromise between noise and power in the design of NAs. The overarching goal is to effectively mitigate this trade-off by introducing novel approaches. The proposed design’s novelty lies in utilizing an adaptive biasing technique instead of the traditional current mirror biasing technique. Furthermore, the conventional input differential pair of the OTA is modified to employ a self cascode flipped voltage follower (SCFVF), which eventually reduces power consumption of the NA to 0.822 [Formula: see text]W. Additionally, notable improvements in noise performance are achieved and found to be 624.9 nV/[Formula: see text] at 1 Hz. The gain and bandwidth range of the input low-noise amplifier is 54.2 dB and 2.8 Hz–207 Hz, respectively, which effectively amplifies low-amplitude noisy incoming signals, addressing the specific requirements of EEG signal acquisition.

Novel design of a low power neural amplifier using split push pull balanced high swing OTA for brain machine interface

Novel design of a low power neural amplifier using split push pull balanced high swing OTA for brain machine interface

Designing a Low-Power Single Stage Neural Amplifier for Efficient Wireless Neural Recording Systems

Designing a Low-Power Single Stage Neural Amplifier for Efficient Wireless Neural Recording Systems

A novel approach for minimising anti-aliasing effects in EEG data acquisition.

Electroencephalography (EEG) waves and other biological signals can be deciphered with a deeper understanding of the human body. The benefits of EEG are growing. EEG studies have expanded globally. Research on EEG covers data gathering, analysis, energy renewal, and more. EEG-gathering devices include encoding, digital transfer, head sensor placement, and separate amplifiers. The EEG detects periodic noise. Head movement, sensor lines, and hair sweat produce low-frequency noise. Low-frequency noise alters EEG signals over time. Muscle actions and electromagnetic waves create high-frequency noise (especially in the facial and neck muscles). EEG shifts are saw-toothed by high-frequency noise. High- and low-frequency noises are usually lower and higher than human EEG, respectively. Lowering signal power above and below the testing level without altering the signs of interest lowers noise. Aliasing may affect low-frequency impacts in the original data because high-frequency noise is mirrored in the data. This work designed a non-binary Complementary metal oxide semiconductor (CMOS) Consecutive guesstimate register (CGR) reconfigurable analogue-to-digital converter (ADC) integrated with the instrumental amplifier. CGR ADC model comprises the bio-signal device monitoring for the EEG signals. This study focused on acquiring the EEG signals for amplification. The model uses the AC-coupled chopper stabilisation model with 1 A low power with a noise level of 1 A. The neural amplifier uses an optimised current technique to maximise the transconductance for a good noise efficiency factor. The simulation analysis estimates a bandwidth range of 0.05-120 Hz with a power consumption level of 0.271 µW. The computed noise level is observed as 1.1 µVrms and a gain of 45 dB. The comparative analysis of the proposed ADC model achieves the minimal energy consumption value of ∼12%, which is minimal than the nonlinear and switch-end capacitor. Also, the time consumed is ∼9% less than the nonlinear and switch-end Capacitor.18 nm CMOS technology is used to implement the proposed data acquisition system for low-power and density-optimised applications.

Research on the Polygraph: the CMOS Neural Amplifier

The polygraph is an effective piece of equipment to detect if the suspect is lying in order to track down the criminal. The paper simulates and sets up the host part of the polygraph in order to explain how the polygraph works and some fields to use it. And we successfully make a neural amplifier to show the way that the host amplifies neural signals, and has a low passband that can filter out high-frequency signals. The circuit has two parts the folded cascode amplifier DC gain is 64.56, the -3dB bandwidth is about 100kHz and the total current consumption is 4uA and the capacitive feedback amplifier passband gain is 40dB and the passband is from about 3Hz to 5kHz. It is of great value not only in the field of criminal investigation, but also in VR, artificial intelligence (AI), and mental health. Because it can record and let people observe human brain activity and emotional changes. What’s more, with its development of it, it can solve many small crimes automatically, which means police can have more time and energy to deal with difficult cases and society can be more stable.

A DC‐coupled 1.2 μW 0.012 mm 2 per channel neural amplifiers array with 75 dB system common mode rejection ratio and 300 mV common mode interference

System common mode rejection ratio (SCMRR) and common mode interference (CMI) range are two critical design specifications for multiple-channel neural recording front-end circuit in a freely moving animal experiment environment. This letter presents a neural amplifiers array consisting of 15 DC-coupled inverter-based recording channels, a reference channel and a shared voltage averaging circuit (VAC) with floating rail Common Mode Feedback (CMFB) to achieve high SCMRR and large CMI range. Meanwhile, power consumption and area are optimized through using simple inverter architecture and removal of the conventional on-chip AC-coupled capacitors. The proposed neural amplifier circuit is implemented in SMIC standard 0.18 μm CMOS process, achieving a typical 75 dB SCMRR (DC-80 Hz), 300 mV CMI range, 1.2 μW power consumption and 0.012 mm2 area per channel. The circuit is validated both by bench-top test and in-vivo animal experiment.

Design of a low-noise low-voltage amplifier for improved neural signal recording.

Design of amplifier circuits with low-noise operable at low-power to be used, especially for implantable neural interfaces, remains a huge challenge. This research paper presents the design of a low-noise low-voltage neural recording amplifier suitable for amplifying local field potentials and extracellular action potentials so as to meet the end requirement of an implantable neuro-medical system. Critical performance parameters of the smaller circuit blocks of the complete neural amplifier architecture have been found with the help of detailed mathematical analysis and then verified by the simulations conducted using 0.18 µm 4M1P foundry Semi-conductor Laboratory N-well process. The neural amplifier design proposed in this paper passes neural signal of interest with a mid-band gain of 49.9 dB over a bandwidth of 5.3 Hz-8.6kHz, draws only 11.5 µW of power from ±0.9V supply voltage, and exhibits an input-referred noise of 2.6 µVrms with a noise efficiency factor of 2.27. The area consumed by the proposed neural amplifier architecture is 0.192mm2. The complete circuit design carried out in this paper should prove to be useful in equipment for the diagnosis of neurological disorders.

818 Association Between Intraoperative LFP, Lead Location, and Outcomes in Subthalamic Nucleus Deep Brain Stimulation for Parkinson's Disease

INTRODUCTION: Imaging-based, LFP-based and empirical programming strategies can be used to determine the optimal therapeutic contact for deep brain stimulation, but the most efficient strategy is unclear. METHODS: In this prospective, IRB-approved study, 10 awake patients were implanted with bilateral STN DBS using both an electrophysiologically- and imaged-confirmed technique. Once the DBS lead had been secured, LFP signals were recorded with a Grapevine Neural Amplifier (Ripple Neuro, UT) at 30 KHz and 16-bit A/D resolution, and LFPs were down-sampled to 2 KHz before further processing. Stereotactic CTs acquired two weeks postoperatively were fused to the pre-operative 3T, T2 weighted MRI and processed using the SureTune platform (Medtronic, MN). Therapeutic DBS programming parameters were obtained 6 months post-implantation, along with pre- and post-operative UPDRS Part III scores. RESULTS: There was a 54% reduction in the UPDRS score when comparing the preoperative OFF score, to the 6 month postoperative OFF Meds/ON Stimulation Score. 50% of the chosen contacts were in the STN only, 10% in the ZI only, and 35% in both. One lead was found to be outside the segmented STN and ZI; this patient did not experience clinical improvement based on their Part III UPDRS score. Beta (8-30Hz) power was 4.1 times higher for electrodes within the STN versus out, 1.4 times for ZI versus out, and 5.9 times higher for both versus neither. The average beta power was 15% higher in the chosen therapeutic contacts over those contacts not used. Lastly, we found that the number of electrodes within the STN correlated with the decrease in the UPDRS score (r=0.27). CONCLUSIONS: There is a strong correlation between image-based electrode localization, LFP characteristics, and clinical outcomes in patients undergoing STN DBS for Parkinson Disease. Focused programming strategies based on either imaging or LFP data can facilitate identification of the optimal therapeutic contact.

Low-Cutoff Frequency Reduction in Neural Amplifiers: Analysis and Implementation in CMOS 65 nm.

Scaling down technology demotes the parameters of AC-coupled neural amplifiers, such as increasing the low-cutoff frequency due to the short-channel effects. To improve the low-cutoff frequency, one solution is to increase the feedback capacitors' value. This solution is not desirable, as the input capacitors have to be increased to maintain the same gain, which increases the area and decreases the input impedance of the neural amplifier. We analytically analyze the small-signal behavior of the neural amplifier and prove that the main reason for the increase of the low-cutoff frequency in advanced CMOS technologies is the reduction of the input resistance of the operational transconductance amplifier (OTA). We also show that the reduction of the input resistance of the OTA is due to the increase in the gate oxide leakage in the input transistors. In this paper, we explore this fact and propose two solutions to reduce the low-cutoff frequency without increasing the value of the feedback capacitor. The first solution is performed by only simulation and is called cross-coupled positive feedback that uses pseudoresistors to provide a negative resistance to increase the input resistance of the OTA. As an advantage, only standard CMOS transistors are used in this method. Simulation results show that a low-cutoff frequency of 1.5 Hz is achieved while the midband gain is 30.4 dB at 1 V. In addition, the power consumption is 0.6 μW. In the second method, we utilize thick-oxide MOS transistors in the input differential pair of the OTA. We designed and fabricated the second method in the 65 nm TSMC CMOS process. Measured results are obtained by in vitro recordings on slices of mouse brainstem. The measurement results show that the bandwidth is between 2 Hz and 5.6 kHz. The neural amplifier has 34.3 dB voltage gain in midband and consumes 3.63 μW at 1 V power supply. The measurement results show an input-referred noise of 6.1 μVrms and occupy 0.04 mm2 silicon area.

Analysis and Reduction of Nonlinear Distortion in AC-Coupled CMOS Neural Amplifiers with Tunable Cutoff Frequencies

Integrated CMOS neural amplifiers are key elements of modern large-scale neuroelectronic interfaces. The neural amplifiers are routinely AC-coupled to electrodes to remove the DC voltage. The large resistances required for the AC coupling circuit are usually realized using MOSFETs that are nonlinear. Specifically, designs with tunable cutoff frequency of the input high‑pass filter may suffer from excessive nonlinearity, since the gate-source voltages of the transistors forming the pseudoresistors vary following the signal being amplified. Consequently, the nonlinear distortion in such circuits may be high for signal frequencies close to the cutoff frequency of the input filter. Here we propose a simple modification of the architecture of a tunable AC-coupled amplifier, in which the bias voltages Vgs of the transistors forming the pseudoresistor are kept constant independently of the signal levels, what results in significantly improved linearity. Based on numerical simulations of the proposed circuit designed in 180 nm technology we analyze the Total Harmonic Distortion levels as a function of signal frequency and amplitude. We also investigate the impact of basic amplifier parameters—gain, cutoff frequency of the AC coupling circuit, and silicon area—on the distortion and noise performance. The post-layout simulations of the complete test ASIC show that the distortion is very significantly reduced at frequencies near the cutoff frequency, when compared to the commonly used circuits. The THD values are below 1.17% for signal frequencies 1 Hz–10 kHz and signal amplitudes up to 10 mV peak-to-peak. The preamplifier area is only 0.0046 mm2 and the noise is 8.3 µVrms in the 1 Hz–10 kHz range. To our knowledge this is the first report on a CMOS neural amplifier with systematic characterization of THD across complete range of frequencies and amplitudes of neuronal signals recorded by extracellular electrodes.

A 2.53 NEF 8-bit 10 kS/s 0.5 μm CMOS Neural Recording Read-Out Circuit with High Linearity for Neuromodulation Implants

This paper presents a power-efficient complementary metal-oxide-semiconductor (CMOS) neural signal-recording read-out circuit for multichannel neuromodulation implants. The system includes a neural amplifier and a successive approximation register analog-to-digital converter (SAR-ADC) for recording and digitizing neural signal data to transmit to a remote receiver. The synthetic neural signal is generated using a LabVIEW myDAQ device and processed through a LabVIEW GUI. The read-out circuit is designed and fabricated in the standard 0.5 μμm CMOS process. The proposed amplifier uses a fully differential two-stage topology with a reconfigurable capacitive-resistive feedback network. The amplifier achieves 49.26 dB and 60.53 dB gain within the frequency bandwidth of 0.57–301 Hz and 0.27–12.9 kHz to record the local field potentials (LFPs) and the action potentials (APs), respectively. The amplifier maintains a noise–power tradeoff by reducing the noise efficiency factor (NEF) to 2.53. The capacitors are manually laid out using the common-centroid placement technique, which increases the linearity of the ADC. The SAR-ADC achieves a signal-to-noise ratio (SNR) of 45.8 dB, with a resolution of 8 bits. The ADC exhibits an effective number of bits of 7.32 at a low sampling rate of 10 ksamples/s. The total power consumption of the chip is 26.02 μμW, which makes it highly suitable for a multi-channel neural signal recording system.

A 2.3-μW Capacitively Coupled Chopper-Stabilized Neural Amplifier With Input Impedance of 6.7 GΩ

Recording electrical activities through high-density electrodes using neural amplifiers plays a crucial role in the successful implementation of a data acquisition system targeting deep-brain regions. To advance the concept of high-density electrodes, neural amplifiers with not only minimum area but also high-power efficiency are needed. This letter presents a neural recording chopper amplifier using the auxiliary path and gain revision techniques to boost the input impedance to a dc value of 6.7 GΩ that meets the requirements of an implantable multichannel recording system. The front-end fabricated in 180-nm CMOS technology occupies an area of 0.051 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , and consumes a total power consumption of 2.1 μW drawn from a 1-V supply. The proposed work increases the dc input impedance ( 18×) and decreases the area by 25% in comparison with conventional auxiliary path impedance boosting with a CMRR of -75 dB, and the input-referred noise of 2.1 μV <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> rms</sub> .

Noise Optimization Techniques for Switched-Capacitor Based Neural Interfaces.

This paper presents the noise optimization of a novel switched-capacitor (SC) based neural interface architecture, and its circuit demonstration in a 0.13[Formula: see text] CMOS process. To reduce thermal noise folding ratio, and suppress kT/C noise, several noise optimization techniques are developed in the proposed architecture. First, one parasitic capacitance suppression scheme is developed to block noise charge transfer from parasitic capacitors to amplifier output. Second, one recording path-splitting scheme is proposed in the input sampling stage to selectively record local field potentials (LFPs), extracellular spikes, or both for reducing input noise floor, and total power consumption. Third, an auto-zero noise cancellation scheme is developed to suppress kT/C noise in the neural amplifier stage. A prototype neural interface chip was fabricated, and also verified in both bench-top, and In-Vivo experiments. Bench-top testings show the input-referred noise of the designed chip is 4.8[Formula: see text] from 1[Formula: see text] to 300[Formula: see text], and 2.3[Formula: see text] from 300[Formula: see text] to 8 kHz respectively, and In-Vivo experiments show the peak-to-peak amplitude of the total noise floor including neural activity, electrode interface noise, and the designed chip is only around 20[Formula: see text]. In comparison with conventional architectures through both circuit measurement and animal experiments, it is well demonstrated that the proposed noise optimization techniques can effectively reduce circuit noise floor, thus extending the application range of switched-capacitor circuits.

Noise‐power‐area optimised design procedure for OTAs with complementary input transistors for neural amplifiers

Simultaneous measurement of neural bio-potentials from a large number of neurons is finding applications in a diverse range of areas such as healthcare and brain-machine interfacing. Neural amplifiers used for these measurements have a tight specification of low-power, low-noise and small area. Most of the reported neural amplifiers use operational transconductance amplifier (OTA) based capacitive feedback amplifiers to meet these requirements. In this study, for the first time, a novel systematic noise-power-area optimised design procedure, for complementary input transistor based OTAs, used frequently in neural amplifiers is proposed. By applying the proposed design procedure, the authors have presented a neural amplifier design that is split into two stages to reduce area requirement and enhance linearity at the expense of slight degradation in noise efficiency factor. The presented design achieves noise in the integration bandwidth of 0.2 Hz–8 kHz with 7.7 μA total current in a die area of in 180 nm CMOS technology. The presented design procedure is inherently technology agnostic. The novelty lies not in the architecture of the proposed neural amplifier, but in the proposed design procedure to optimise the noise-power-area trade-off in CMOS amplifier circuit design.

Design and simulation of pseudo-resistor with extremely high linearity for an improved neural signal recording.

Design of a pseudo-resistor (PR) structure based on a bulk driven quasi floating technique is presented, which not only provides a high value of resistance (≈1 TΩ) over a wider voltage swing of -1 V to 1 V, but also displays extremely high linearity. The same is useful for accurate amplification and recording of neural signals for better diagnosis of many chronic diseases. PR design reported in this paper has been simulated in 0.18 µm technology with BSIM3V3 MOS device models in the 6M1P standard process. The proposed PR has been simulated for its accuracy through its use in the design of the neural amplifier with a display of better performance than earlier reported work.

Multi-Channel Neural Recording Implants: A Review.

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.

改进蚁群算法的BRBP神经网络功放逆向建模方法

改进蚁群算法的BRBP神经网络功放逆向建模方法

A low-power digitally programmable neural recording amplifier with selectable multiunit activity (MUA) and local field potential (LFP) recording modes

This paper presents the design of a low-power implantable neural recording amplifier in a 0.13 μm CMOS technology. The structure consists of a cascade of 4-stages, including instrumentation amplifier (IA), buffer, high pass filter (HPF) and programmable gain amplifier (PGA). The proposed neural amplifier can select between multiunit activity (MUA) and local field potential (LFP) recording modes. A programmable gain is available in the MUA recording mode. Eight gain states can be selected from 58.4 dB to 79.5 dB with 3-bit digital control signals. The digital control signals can be made by a digital signal processing (DSP) unit. In MUA recording mode, the bandwidth is measured from 480 Hz to 8.7 kHz. In LFP recording mode, a constant midband gain of 58.4 dB is achieved in a passband of 0.36 Hz–330 Hz. A noise efficiency factor (NEF) of 1.47 is obtained for the amplifier. Power dissipation is decreased to 1.9 μW per recording channel, by biasing the transistors in the subthreshold region. The proposed neural amplifier can be used for electrocorticography (ECoG) recording in wireless implantable devices.

A Compact Quad-Shank CMOS Neural Probe With 5,120 Addressable Recording Sites and 384 Fully Differential Parallel Channels.

Large-scale invivo electrophysiology requires tools that enable simultaneous recording of multiple brain regions at single-neuron level. This calls for the design of more compact neural probes that offer even larger arrays of addressable sites and high channel counts. With this aim, we present in this paper a quad-shank approach to integrate as many as 5,120 sites on a single probe. Compact fully-differential recording channels were designed using a single-gain-stage neural amplifier with a 14-bit ADC, achieving a mean input-referred noise of 7.44μVrms in the action-potential band and 7.65μVrms in the local-field-potential band, a mean total harmonic distortion of 0.17% at 1 kHz and a mean input-referred offset of 169μV. The probe base incorporates 384 channels with on-chip power management, reference-voltage generation and digital control, thus achieving the highest level of integration in a neural probe and excellent channel-to-channel uniformity. Therefore, no calibration or external circuitry are required to achieve the above-mentioned performance. With a total area of 2.2×8.67mm2 and a power consumption of 36.5 mW, the presented probe enables full-system miniaturization for acute or chronic use in small rodents.

Design and Implement of High Gain and Low Noise Neural Amplifier Using Compensation Techniques

&lt;em&gt;Electroencephalography is refer to record the electrical signal with respect to brain activity and its reliable EEG information, using this to diagnosis disorder and tumors. However the signal is very difficult to capture and processing due to so many parameter. Mainly this signal is very low range that from 0.1 to 100μv in and its bandwidth range from 1Hz to 100 Hz. So the signal has amplified by using linear and accurate digital program amplifier(PGA).This amplifier has been designed by using First stage amplifier with gain of 120dB with low output noise. The PGA is consists of OPAMPs the PGA change from 10db to 120dB.Inorde to optimized the linear and gain accuracy a new structure resister array is proposed high gain PGA. Hence the simulated result has shown it is promising to exhibit an amplifier with high performance biomedical application.&lt;/em&gt;