A TDM-Based 16-Channel AFE ASIC With Enhanced System-Level CMRR for Wearable EEG Recording With Dry Electrodes

A Current-Based EEG Amplifier and Validation With a Saline Phantom and an SSVEP Paradigm

OBJECTIVE

This work describes current-mode electroencephalography amplifiers to record the electrical activity of the tangentially oriented cells. Electroencephalography (EEG) measures the summed electrical activity from pyramidal cells in the brain by using non-invasive electrodes placed on the scalp. Traditional, voltage-based measurements are done with differential amplifiers. Depending on the location of the electrodes used for the differential measurement, EEG can estimate electrical activity from radially (common or average reference) or tangentially (bipolar derivation) oriented neurons. A limitation of the bipolar derivation is that when the electrodes are too close together, the conductive solution used to improve electrode-skin impedance can short-circuit the electrodes. Magnetoencephalography (MEG) also enables measurements from tangentially oriented cells without concerns about short-circuiting the electrodes. However, MEG is a more expensive, and a less available technology. Measuring from both radial and tangential cells can improve the resolution to localize the origin of brain activity; this could be extremely useful for diagnoses and treatment of several neurological disorders.

METHODS

Circuit design from previous implementations was improved and the device was compared to a voltage-based (vEEG) amplifier in a saline phantom and in humans with a steady state visually evoked potentials paradigm.

RESULTS

The current-based (cEEG) amplifier satisfied suggested electrical parameters for EEG amplifiers and exhibited higher sensitivity to tangential dipoles in the phantom study. It measured brain activity using the same scalp electrodes as vEEG amplifiers with comparable performance.

CONCLUSION

current-based EEG amplifiers can be comparable to traditional voltage-based amplifiers and offer complementary information.

A Wirelessly Powered Scattered Neural Recording Wearable System

This paper introduces a wirelessly powered scattered neural recording wearable system that can facilitate continuous, untethered, and long-term electroencephalogram (EEG) recording. The proposed system, including 32 standalone EEG recording devices and a central controller, is incorporated in a wearable form factor. The standalone devices are sparsely distributed on the scalp, allowing for flexible placement and varying quantities to provide extensive spatial coverage and scalability. Each standalone device featuring a low-power EEG recording application-specific integrated circuit (ASIC) wirelessly receives power through a 60 MHz inductive link. The low-power ASIC design (84.6 µW) ensures sufficient wireless power reception through a small receiver (Rx) coil. The 60 MHz inductive link also serves as the data carrier for wireless communication between standalone devices and the central controller, eliminating the need for additional data antennas. All these efforts contribute to the miniaturization of standalone devices with dimensions of 12 × 12 × 5 mm3, enhancing device wearability. The central controller applies the pulse width modulation (PWM) scheme on the 60 MHz carrier, transmitting user commands at 4 Mbps to EEG recording ASICs. The ASIC employs a novel synchronized PWM demodulator to extract user commands, operating signal digitization and data transmission. The analog frontend (AFE) amplifies the EEG signal with a gain of 45 dB and applies band-pass filtering from 0.03 Hz to 400 Hz, with an input-referred noise (IRN) of 3.62 µVRMS. The amplified EEG signal is then digitized by a 10-bit successive approximation register (SAR) analog-to-digital converter (ADC) with a peak signal-to-noise and distortion ratio (SNDR) of 55.4 dB. The resulting EEG data is transmitted to an external software-defined radio (SDR) Rx through load-shift-keying (LSK) backscatter at 3.75 Mbps. The system's functionality is fully evaluated in human experiments.

A LFP/AP Mode Reconfigurable Analog Front-End Combining an Electrical EEEG-iEEG Model for the Closed-Loop VNS

This article presents a local field potential (LFP)/action potential (AP) mode reconfigurable analog front-end (AFE) dedicated for the closed-loop vagus nerve stimulator (VNS). It combines an inverse electrical model of the intracranial electroencephalogram (iEEG) conducting in the brain tissues and been recorded at scalp as the extended electroencephalogram (EEEG). The AFE contains a LFP/AP mode reconfigurable EEEG preamplifier, a tunable integrator to compensate the effect of either the recording electrodes or head tissues, and an adder. The LFP/AP mode reconfigurable EEEG preamplifier consists of a tunable chopper-stabilized amplifier (CSA) and a 2nd-order tunable low pass filter (LPF). For better separation of LFP and AP signals, a high-order DC servo loop (DSL) characterized as a 2nd-order DSL in parallel with a 1st-order DSL is exploited in the tunable CSA to achieve a tunable high-pass frequency with a stopband attenuation slope (SAS) of +40 dB/dec. In addition, the tunable LPF can obtain a tunable low-pass frequency with a SAS of -40 dB/dec and provide additional 20 dB gain for AP signals. Fabricated in a SMIC 180 nm CMOS technology, and in the LFP band (0.5 Hz-200 Hz) and AP band (300 Hz-5 kHz), the measured mid-band gains of the LFP/AP mode reconfigurable EEEG preamplifier are 39.6 dB and 59.5 dB, the input-referred noises (IRNs) are 2.2 μVrms and 6.3 μVrms, the DC/in-band input impedances are 1.27/1.26 GΩ and 0.3/0.22 GΩ, respectively. The power consumption per channel AFE is 6.3 μW, and the die area is 1.4 mm × 0.25 mm.

A High CMRR Differential Difference Amplifier Employing Combined Input Pairs for Neural Signal Recordings

This article introduces a Combined .symmetrical and complementary Input Pairs (CIP) of a Differential Difference Amplifier (DDA), to boost the total Common-Mode Rejection Ratio (CMRR) for multi-channel neural signal recording. The proposed CIP-DDA employs three input pairs (transconductors). The dc-coupled input neural signal connection, via the gate terminal of the first transconductor, yields a high input impedance. The high-pass corner frequency and dc quiescent operation point are stabilized by the second transconductor. The calibration path of differential-mode gain and Common-Mode Feedback (CMFB) is provided by the proposed third transconductor. The parallel connection has no need for extra voltage headroom of input and output. The proposed CIP-DDA is targeted at integrated circuit realization and designed in a 0.18-μm CMOS technology. The proposed CIP-DDAs with system CMFB achieve an average CMRR of 103 dB, and each channel consumes circa 3.6 μW power consumption.

A 1.5 mm2 4-Channel EEG/BIOZ Acquisition ASIC With 15.2-Bit 3-Step ADC Based on a Signal-Dependent Low-Power Strategy

This article presents a multichannel EEG/BIOZ acquisition application specific integrated circuit (ASIC) with 4 EEG channels and a BIOZ channel, a switch resistor low-pass filter (SR-LPF). Each EEG channel includes a frontend, and a 4-channel multiplexed analog-to-digital converter (ADC), while the BIOZ channel features a pseudo sine current generator and a pair of readout paths with multiplexed SR-LPF and ADC. The ASIC is designed for size and power minimization, utilizing a 3-step ADC with a novel signal-dependent low power strategy. The proposed ADC operates at a sampling rate of 1600 S/s with a resolution of 15.2 bits, occupying only 0.093 mm2. With the help of the proposed signal-dependent low-power strategy, the ADC's power dissipation drops from 32.2 μW to 26.4 μW, resulting in an 18% efficiency improvement without performance degradation. Moreover, the EEG channels deliver excellent noise performance with a NEF of 7.56 and 27.8 nV/√Hz at the expense of 0.16 mm2 per channel. In BIOZ measurement, a 5-bit programmable current source is used to generate pseudo sine injection current ranging from 0 to 22 μApp, and the detection sensitivity reaches 2.4 mΩ/√Hz. Finally, the presented multichannel EEG/BIOZ acquisition ASIC has a compact active area of 1.5 mm2 in an 180nm CMOS technology.

A 4.43 TΩ ZIN 0.0128 mm2 Cascaded Instrumentation Amplifier with Input-biased Pseudo Resistor for Implantable Brain Machine Interfaces

A cascaded instrumentation amplifier (CaIA) with input-biased pseudo resistors (IBPR) is presented for implantable brain machine interfaces (BMI). The gain distribution of two-stage cascaded amplifiers, instead of a single-stage amplifier, helps to achieve an input impedance of 4.43TΩ at 100Hz, and maintain the small active area (0.0128 mm2). The input-biased pseudo resistors contribute to a much lower high-pass corner (fHP=0.00011Hz) compared with the conventional structure, the input-referred noise is only 3.836μVrms integrated from 0.5Hz to 10kHz with 0.98μW power consumption.Clinical Relevance- This establishes an area-efficient amplifier design with ultra-high input impedance (4.43TΩ at 100Hz) and hyper-low high-pass corner frequency (fHP=0.00011Hz), which is suitable for long-term monitoring of neural activities (including slow oscillations) in implantable brain-machine interfaces.

Progress in Data Acquisition of Wearable Sensors

Wearable sensors have demonstrated wide applications from medical treatment, health monitoring to real-time tracking, human-machine interface, smart home, and motion capture because of the capability of in situ and online monitoring. Data acquisition is extremely important for wearable sensors, including modules of probes, signal conditioning, and analog-to-digital conversion. However, signal conditioning, analog-to-digital conversion, and data transmission have received less attention than probes, especially flexible sensing materials, in research on wearable sensors. Here, as a supplement, this paper systematically reviews the recent progress of characteristics, applications, and optimizations of transistor amplifiers and typical filters in signal conditioning, and mainstream analog-to-digital conversion strategies. Moreover, possible research directions on the data acquisition of wearable sensors are discussed at the end of the paper.

Multi-Channel Biopotential Acquisition System Using Frequency-Division Multiplexing With Cable Motion Artifact Suppression

A multi-channel, CMOS-based biopotential acquisition system is presented that uses amplitude modulated, frequency division multiplexing (AM-FDM) to decrease wire count and provide resilience against motion artifacts and cable noise. Differential active electrode (AE) pairs capture surface biopotential signals, each modulated by a different carrier frequency and combined via current-domain summing. The presented approach requires only a single wire for signal transmission between AEs and back-end readout, along with clock and ground wires, to support multiple active electrodes using a 3-wire cable. Frequency modulation prior to transmission mitigates the effect of low-frequency cable motion artifacts and 50/60 Hz mains interference in the cable. A prototype FDM-based biopotential acquisition system was implemented in a 180 nm CMOS process, including a four-channel front-end active electrode IC for signal conditioning and modulation, and a back-end IC for demodulation and digitization. Each channel occupies 0.75 mm [Formula: see text] and consumes 43.8 μ W, inclusive of ADC power. Using both AE and BE ICs, a four-channel biopotential recording system is demonstrated using a 3-wire interface, where the system achieves attenuation of low-frequency cable motion artifacts by 15X and 60 Hz mains noise coupled into the cable by 62X.

A 10.13µJ/Classification 2-Channel Deep Neural Network Based SoC for Negative Emotion Outburst Detection of Autistic Children

An electroencephalogram (EEG)-based non-invasive 2-channel neuro-feedback SoC is presented to predict and report negative emotion outbursts (NEOB) of Autistic patients. The SoC incorporates area-and-power efficient dual-channel Analog Front-End (AFE), and a deep neural network (DNN) emotion classification processor. The classification processor utilizes only the two-feature vector per channel to minimize the area and overfitting problems. The 4-layers customized DNN classification processor is integrated on-sensor to predict the NEOB. The AFE comprises two entirely shared EEG channels using sampling capacitors to reduce the area by 30%. Moreover, it achieves an overall integrated input-referred noise, NEF, and crosstalk of 0.55 µV_RMS, 2.71, and -79 dB, respectively. The 16 mm² SoC is implemented in 0.18 um 1P6M, CMOS process and consumes 10.13 μJ/classification for 2 channel operation while achieving an average accuracy of >85% on multiple emotion databases and real-time testing.

An Active Concentric Electrode for Concurrent EEG Recording and Body-Coupled Communication (BCC) Data Transmission

This paper presents a wearable active concentric electrode for concurrent EEG monitoring and Body-Coupled Communication (BCC) data transmission. A three-layer concentric electrode eliminates the usage of wires. A common mode averaging unit (CMAU) is proposed to cancel not only the continuous common-mode interference (CMI) but also the instantaneous CMI of up to 51V_pp. The localized potential matching technique removes the ground electrode. An open-loop programmable gain amplifier (OPPGA) with the pseudo-resistor-based RC-divider block is presented to save the silicon area. The presented work is the first reported so far to achieve the concurrent EEG signal recording and BCC-based data transmission. The proposed chip achieves 100 dB CMRR and 110 dB PSRR, occupies 0.044 mm², and consumes 7.4 μW with an input-referred noise density of 26 nV/√Hz.