A 0.67 µV-IIRN super-T Ω-Z IN 17.5 µW/Ch Active Electrode With In-Channel Boosted CMRR for Distributed EEG Monitoring.

We present the design, development, and experimental characterization of an active electrode (AE) IC for wearable ambulatory EEG recording. The proposed architecture features in-AE double common-mode (CM) rejection, making the recording's CMRR independent of typically-significant AE-to-AE gain variations. Thanks to being DC coupled and needless of chopper stabilization for flicker noise suppression, the architecture yields a super-T Ω input impedance. Such a large input impedance makes the AE's CMRR practically immune to electrode-skin interface impedance variations across different recording channels, a critical feature for dry-electrode ambulatory systems. Signal quantization and serialization are also performed in-AE, which enables a distributed system in which all AEs use a single data bus for data/command communication to the backend module, thus significantly improving the system's scalability. Additionally, the presented AE hosts auxiliary modules for (i) detection of an unstable electrode-skin connection through continuous interface impedance monitoring, (ii) dynamic measurement and adjustment of input DC level, and (iii) a CM feedback loop for further CMRR enhancement. The article also presents the development of printed (extrusion) tattoo electrodes and their experimental characterization results with the proposed AE architecture. Besides bio-compatibility, low-cost, pattern flexibility, and quick fabrication process, the printed electrodes offer a very stable electrode-skin connection, conform to scalp shape, and exhibit consistent performance under various bending curvatures. Analog circuit blocks of the presented AE architecture are designed and fabricated using a standard 180 nm CMOS technology, and the [Formula: see text] IC is integrated with off-chip low-power digital modules on a PCB to form the AE. Our measurement results show a CMRR of 82.2 dB (at 60 Hz), amplification voltage gain of 52.8 dB, a bandwidth of 0.2-400 Hz, ±500 mV input DC offset tolerance, An input impedance [Formula: see text], and 0.67 µV RMS integrated input referred noise (0.5-100 Hz), while consuming 17.5 µW per channel. All auxiliary modules are tested experimentally, and the entire system is validated in-vivo, for both ECG and EEG recording.

An 8-Channel Ambulatory EEG Recording IC With In-Channel Fully-Analog Real-Time Motion Artifact Extraction and Removal.

Wereport the design, implementation, and experimental characterization of an 8-channel EEG recording IC (0.13 µm CMOS, 12 mm 2 total area) with a channel architecture that conducts both the extraction and removal of motion artifacts on-chip and in-channel. The proposed dual-path feed-forward method for artifact extraction and removal is implemented in the analog domain, hence is needless of a DSP unit for artifact estimation, and its associated high-DR ADCs and DACs employed by the state of the art for artifact replica generation. Additionally, the presented architecture improves system's scalability as it enables channels' stand-alone operation, and yields the lowest reported channel power consumption among works featuring motion artifact detection/removal. Following an experimental study on electrode-skin interface electrical characteristics for dry electrodes in the absence and presence of motions, the article presents the channel architecture, its detailed signal transfer function analysis, circuit-level implementation, and experimental characterization results. Our measurement results show an amplification voltage gain of 48.3 dB, a bandwidth of 300 Hz, rail-to-rail input DC offset tolerance, and 41.5 dB artifact suppression, while consuming 55 µW per channel. The system's efficacy in EEG motion artifact suppression is validated experimentally, and system- and circuit-level features and performance metrics of the presented design are compared with the state of the art.

A 21.3%-Efficiency Clipped-Sinusoid UWB Impulse Radio Transmitter With Simultaneous Inductive Powering and Data Receiving.

An ultra-wide-band impulse-radio (UWB-IR) transmitter (TX) for low-energy biomedical microsystems is presented. High power efficiency is achieved by modulating an LC tank that always resonates in the steady state during transmission. A new clipped-sinusoid scheme is proposed for on-off keying (OOK)-modulation, which is implemented by a voltage clipper circuit with on-chip biasing generation. The TX is designed to provide a high data-rate wireless link within the 3-5 GHz band. The chip was fabricated in 130 nm CMOS technology and fully characterized. State-of-the-art power efficiency of 21.3% was achieved at a data-rate of 230 Mbps and energy consumption of 21pJ/b. A bit-error-rate (BER) of less than 10 -6 was measured at a distance of 1 m without pulse averaging. In addition, simultaneous wireless powering and VCO-based data transmission are supported. A potential extension to a VCO-free all-wireless mode to further reduce the power consumption is also discussed.

An Implantable Optogenetic Neuro-Stimulator SoC With Extended Optical Pulse-Width Enabled by Supply-Variation-Immune Cycled Light-Toggling Stimulation.

The design, development, and experimental validation of an inductively-powered four-channel optical neuro-stimulator system on a chip (SoC) with on-chip neural recording, temperature monitoring, signal processing, and bidirectional wireless data communication are presented. A biologically-inspired optical stimulation approach is employed that extends the limitations on the stimulation pulse-width and frequency (i.e., enabling wirelessly-powered optical stimulation at very low frequencies (e.g., 10 Hz)) while significantly reducing the required on-device storage capacitor size. The biological efficacy of the proposed approach is validated and compared with conventional stimulation through in vitro experiments. The stimulator's energy efficiency is enhanced by employing a high-gain (850 A/A) current amplifier/driver in each channel that steers up to 10 mA into the optical source with an excellent linearity ( 0.5LSB), while 1) yielding the lowest-in-literature required voltage headroom, and 2) being insensitive to large (up to 12%) supply voltage drops, which is ideal for battery-less implantable devices. Additionally, to maximize the percentage of the generated optical power that reaches the targeted cells (thus, further energy efficiency enhancement), inkjet printing is utilized to fabricate custom-designed optical µlenses that are placed directly on top of the silicon SoC to enhance the generated light's directivity by > 30×. An electrophysiological recording channel for real-time monitoring of the stimulation efficacy and a high-precision (0.1 °C resolution) temperature readout circuit for shutting off stimulation upon detection of an unsafe temperature increase are also integrated on the chip. Additionally, the SoC hosts an ASK receiver and an LSK transmitter for downlink and uplink wireless data communication, respectively. The SoC is fabricated in a standard 130 nm CMOS process and occupies 6 mm 2. Measurement results for different sensory and communication blocks are presented, as well as in vitro experimental validation results showing simultaneous optical stimulation, electrical recording, and calcium imaging.

Motion-Affected Electrode-Tissue Interface Characterization for Ambulatory EEG Recording.

Motion artifacts are arguably the most important issue in the development of wearable ambulatory EEG devices. Designing circuits and systems capable of high-quality EEG recording regardless of these artifacts requires a clear understanding of how the electrode-skin interface is affected by physical motions. In this work, first, we report statistically-significant experimental characterization results of electrodeskin interface impedance for dry contact and non-contact electrodes in the presence of various motions. This leads to a model describing the motion-induced electrode-skin interface impedance variations for these electrodes. Next, a critical review of the possible analog front-end circuits for surface EEG recording is presented, followed by theoretical circuit analysis discussing the effect of electrode movements on the operation of these circuits. Inspired by the developed model and the analytical review, a novel front-end architecture capable of extracting motion from the EEG signal during the amplification stage is presented and experimentally characterized.

An Energy-Efficient Optically-Enhanced Highly-Linear Implantable Wirelessly-Powered Bidirectional Optogenetic Neuro-Stimulator.

This paper presents an energy-efficient mm-scale self-contained bidirectional optogenetic neuro-stimulator, which employs a novel highly-linear µLED driving circuit architecture as well as inkjet-printed custom-designed optical µlenses for light directivity enhancement. The proposed current-mode µLED driver performs linear control of optical stimulation for the entire target range ( 10 mA) while requiring the smallest reported headroom, yielding a significant boost in the energy conversion efficiency. A 30.46× improvement in the power delivery efficiency to the target tissue is achieved by employing a pair of printed optical µlenses. The fabricated SoC also integrates two recording channels for LFP recording and digitization, as well as power management blocks. A micro-coil is also embedded on the chip to receive inductive power and our experimental results show a PTE of 2.24 % for the wireless link. The self-contained system including the µLEDs, µlenses and the capacitors required by the power management blocks is sized 6 mm 3 and weighs 12.5 mg. Full experimental measurement results for electrical and optical circuitry as well as in vitro measurement results are reported.

A Resource-Optimized VLSI Implementation of a Patient-Specific Seizure Detection Algorithm on a Custom-Made 2.2 cm 2 Wireless Device for Ambulatory Epilepsy Diagnostics.

A patient-specific epilepsy diagnostic solution in the form of a wireless wearable ambulatory device is presented. First, the design, VLSI implementation, and experimental validation of a resource-optimized machine learning algorithm for epilepsy seizure detection are described. Next, the development of a mini-PCB that integrates a low-power wireless data transceiver and a programmable processor for hosting the seizure detection algorithm is discussed. The algorithm uses only EEG signals from the frontal lobe electrodes while yielding a seizure detection sensitivity and specificity competitive to the standard full EEG systems. The experimental validation of the algorithm VLSI implementation proves the possibility of conducting accurate seizure detection using quickly-mountable dry-electrode headsets without the need for uncomfortable/painful through-hair electrodes or adhesive gels. Details of design and optimization of the algorithm, the VLSI implementation, and the mini-PCB development are presented and resource optimization techniques are discussed. The optimized implementation is uploaded on a low-power Microsemi Igloo FPGA, requires 1237 logic elements, consumes 110 µW dynamic power, and yields a minimum detection latency of 10.2 µs. The measurement results from the FPGA implementation on data from 23 patients (198 seizures in total) shows a seizure detection sensitivity and specificity of 92.5% and 80.1%, respectively. Comparison to the state of the art is presented from system integration, the VLSI implementation, and the wireless communication perspectives.

A 9.2-g Fully-Flexible Wireless Ambulatory EEG Monitoring and Diagnostics Headband With Analog Motion Artifact Detection and Compensation.

An 8-channel wearable wireless device for ambulatory surface EEG monitoring and analysis is presented. The entire multi-channel recording, quantization, and motion artifact removal circuitries are implemented on a 4-layer polyimide flexible substrate. The recording electrodes and active shielding are also integrated on the same substrate, yielding the smallest form factor compared to the state of the art. Thanks to the dry non-contact electrodes, the system is quickly mountable with minimal assistance required, making it an ideal ambulatory front- and temporal-lobe EEG monitoring device. The flexible main board is connected to a rechargeable battery on one end and to a 13 × 17 mm 2 rigid board on the other end. The mini rigid board hosts a low-power programmable FPGA and a BLE 5.0 transceiver, which add diagnostic capability and wireless connectivity features to the device, respectively. Design considerations for a wearable EEG monitoring and diagnostic device are discussed in details. The theory of the novel fully-analog method for motion artifact detection and removal is described and the detailed circuit implementation is presented. The device performance in terms of voltage gain (260 V/V), bandwidth (DC-300 Hz), motion artifact removal, and wireless communication throughput (up to 1 Mbps) is experimentally validated. The entire wearable solution with the battery weighs 9.2 grams.

Arbitrary-Waveform Electro-Optical Intracranial Neurostimulator With Load-Adaptive High-Voltage Compliance.

A hybrid 16-channel current-mode and the 8-channel optical implantable neurostimulating system is presented. The system generates arbitrary-waveform charge-balanced current-mode electrical pulses with an amplitude ranging from 50 [Formula: see text] to 10 mA. An impedance monitoring feedback loop is employed to automatically adjust the supply voltage, yielding a load-optimized power dissipation. The 8-channel optical stimulator drives an array of LEDs, each with a maximum of 25 mA current amplitude, and reuses the arbitrary-waveform generation function of the electrical stimulator. The LEDs are assembled within a custom-made 4×4 ECoG grid electrode array, enabling precise optical stimulation of neurons with a 300 [Formula: see text] pitch between the LEDs and simultaneous monitoring of the neural response by the ECoG electrode, at different distances of the stimulation site. The hybrid stimulation system is implemented on a mini-PCB, and receives power and stimulation commands inductively through a second board and a coil stacked on top of it. The entire system is sized at 3×2 . 5×1 cm3 and weighs 7 grams. The system efficacy for electrical and optical stimulation is validated in-vivo using separate chronic and acute experiments.

Closed-Loop Neurostimulators: A Survey and A Seizure-Predicting Design Example for Intractable Epilepsy Treatment.

First, existing commercially available open-loop and closed-loop implantable neurostimulators are reviewed and compared in terms of their targeted application, physical size, system-level features, and performance as a medical device. Next, signal processing algorithms as the primary strength point of the closed-loop neurostimulators are reviewed, and various design and implementation requirements and trade-offs are discussed in details along with quantitative examples. The review results in a set of guidelines for algorithm selection and evaluation. Second, the implementation of an inductively-powered seizure-predicting microsystem for monitoring and treatment of intractable epilepsy is presented. The miniaturized system is comprised of two miniboards and a power receiver coil. The first board hosts a 24-channel neurostimulator system on chip fabricated in a [Formula: see text] CMOS technology and performs neural recording, on-chip digital signal processing, and electrical stimulation. The second board communicates recorded brain signals as well as signal processing results wirelessly. The multilayer flexible coil receives inductively-transmitted power. The system is sized at 2 × 2 × 0.7 [Formula: see text] and weighs 6 g. The approach is validated in the control of chronic seizures in vivo in freely moving rats.

Electronic Sleep Stage Classifiers: A Survey and VLSI Design Methodology.

First, existing sleep stage classifier sensors and algorithms are reviewed and compared in terms of classification accuracy, level of automation, implementation complexity, invasiveness, and targeted application. Next, the implementation of a miniature microsystem for low-latency automatic sleep stage classification in rodents is presented. The classification algorithm uses one EMG (electromyogram) and two EEG (electroencephalogram) signals as inputs in order to detect REM (rapid eye movement) sleep, and is optimized for low complexity and low power consumption. It is implemented in an on-board low-power FPGA connected to a multi-channel neural recording IC, to achieve low-latency (order of 1 ms or less) classification. Off-line experimental results using pre-recorded signals from nine mice show REM detection sensitivity and specificity of 81.69% and 93.86%, respectively, with the maximum latency of 39 [Formula: see text]. The device is designed to be used in a non-disruptive closed-loop REM sleep suppression microsystem, for future studies of the effects of REM sleep deprivation on memory consolidation.

Rapid brief feedback intracerebral stimulation based on real-time desynchronization detection preceding seizures stops the generation of convulsive paroxysms.

OBJECTIVE: To investigate the abortion of seizure generation using "minimal" intervention in hippocampi using two rat models of human temporal lobe epilepsy. METHODS: The recording or stimulation electrodes were implanted into both hippocampi (CA1 area). Using the kainic acid (chronic: experiment duration 24 days) and the 4-aminopyridine (acute: experiment duration 2 h) models of paroxysms in rats, a real-time feedback stimulation paradigm was implemented, which triggered a short periodic electrical stimulus (5 Hz for 5 s) upon detecting a seizure precursor. Our seizure precursor detection algorithm relied on the monitoring of the real-time phase synchronization analysis, and detected/anticipated electrographic seizures as early as a few seconds to a few minutes before the behavioral and electrographic seizure onset, with a very low false-positive rate of the detection. RESULTS: The baseline mean seizure frequencies were 5.39 seizures per day (chronic) and 13.2 seizures per hour (acute). The phase synchrony analysis detected 88% (434 of 494) of seizures with a mean false alarm of 0.67 per day (chronic) and 83% (86 of 104) of seizures with a mean false alarm of 0.47 per hour (acute). The feedback stimulation reduced the seizure frequencies to 0.41 seizures per day (chronic) and 2.4 seizures per hour (acute). Overall, the feedback stimulation paradigm reduced seizure frequency by a minimum of 80% to a maximum of 100% in 10 rats, with 83% of the animals rendered seizure-free. SIGNIFICANCE: This approach represents a simple and efficient manner for stopping seizure development. Because of the short on-demand stimuli, few or no associated side effects are expected in clinical application in patients with epilepsy. Abnormal synchrony patterns are common features in epilepsy and other neurologic and psychiatric syndromes; therefore, this type of feedback stimulation paradigm could be a novel therapeutic modality for use in various neurologic and psychiatric disorders.