Wireless multichannel biopotential recording using an integrated FM telemetry circuit

BRAINsens: Body-Worn Reconfigurable Architecture of Integrated Network Sensors

Body sensor network (BSN) is a promising human-centric technology to monitor neurophysiological data. We propose a fully-reconfigurable architecture that addresses the major challenges of a heterogenous BSN, such as scalabiliy, modularity and flexibility in deployment. Existing BSNs especially with Electroencephalogarm (EEG) have these limitations mainly due to the use of driven-right-leg (DRL) circuit. We address these limitations by custom-designing DRL-less EEG smart sensing nodes (SSN) for modular and spatially distributed systems. Each single-channel EEG SSN with a input-referred noise of 0.82 μV_rms and CMRR of 70 dB (at 60 Hz), samples brain signals at 512 sps. SSNs in the network can be configured at the time of deployment and can process information locally to significantly reduce data payload of the network. A Control Command Node (CCN) initializes, synchronizes, periodically scans for the available SSNs in the network, aggregates their data and sends it wirelessly to a paired device at a baud rate of 115.2 kbps. At the given settings of the I²C bus speed of 100 kbps, CCN can configure up to 39 EEG SSNs in a lego-like platform. The temporal and frequency-domain performance of the designed "DRL-less" EEG SSNs is evaluated against a research-grade Neuroscan and consumer-grade Emotiv EPOC EEG. The results show that the proposed network system with wearable EEG can be deployed in situ for continuous brain signal recording in real-life scenarios. The proposed system can also seamlessly incorporate other physiological SSNs for ECG, HRV, temperature etc. along with EEG within the same topology.

A CMOS IC-based multisite measuring system for stimulation and recording in neural preparations in vitro

In this report, we describe the system integration of a complementary metal oxide semiconductor (CMOS) integrated circuit (IC) chip, capable of both stimulation and recording of neurons or neural tissues, to investigate electrical signal propagation within cellular networks in vitro. The overall system consisted of three major subunits: a 5.0 × 5.0 mm CMOS IC chip, a reconfigurable logic device (field-programmable gate array, FPGA), and a PC. To test the system, microelectrode arrays (MEAs) were used to extracellularly measure the activity of cultured rat cortical neurons and mouse cortical slices. The MEA had 64 bidirectional (stimulation and recording) electrodes. In addition, the CMOS IC chip was equipped with dedicated analog filters, amplification stages, and a stimulation buffer. Signals from the electrodes were sampled at 15.6 kHz with 16-bit resolution. The measured input-referred circuitry noise was 10.1 μ V root mean square (10 Hz to 100 kHz), which allowed reliable detection of neural signals ranging from several millivolts down to approximately 33 μ Vpp. Experiments were performed involving the stimulation of neurons with several spatiotemporal patterns and the recording of the triggered activity. An advantage over current MEAs, as demonstrated by our experiments, includes the ability to stimulate (voltage stimulation, 5-bit resolution) spatiotemporal patterns in arbitrary subsets of electrodes. Furthermore, the fast stimulation reset mechanism allowed us to record neuronal signals from a stimulating electrode around 3 ms after stimulation. We demonstrate that the system can be directly applied to, for example, auditory neural prostheses in conjunction with an acoustic sensor and a sound processing system.

Integrated wireless neural interface based on the Utah electrode array

This report presents results from research towards a fully integrated, wireless neural interface consisting of a 100-channel microelectrode array, a custom-designed signal processing and telemetry IC, an inductive power receiving coil, and SMD capacitors. An integration concept for such a device was developed, and the materials and methods used to implement this concept were investigated. We developed a multi-level hybrid assembly process that used the Utah Electrode Array (UEA) as a circuit board. The signal processing IC was flip-chip bonded to the UEA using Au/Sn reflow soldering, and included amplifiers for up to 100 channels, signal processing units, an RF transmitter, and a power receiving and clock recovery module. An under bump metallization (UBM) using potentially biocompatible materials was developed and optimized, which consisted of a sputter deposited Ti/Pt/Au thin film stack with layer thicknesses of 50/150/150 nm, respectively. After flip-chip bonding, an underfiller was applied between the IC and the UEA to improve mechanical stability and prevent fluid ingress in in vivo conditions. A planar power receiving coil fabricated by patterning electroplated gold films on polyimide substrates was connected to the IC by using a custom metallized ceramic spacer and SnCu reflow soldering. The SnCu soldering was also used to assemble SMD capacitors on the UEA. The mechanical properties and stability of the optimized interconnections between the UEA and the IC and SMD components were measured. Measurements included the tape tests to evaluate UBM adhesion, shear testing between the Au/Sn solder bumps and the substrate, and accelerated lifetime testing of the long-term stability for the underfiller material coated with a a-SiC(x):H by PECVD, which was intended as a device encapsulation layer. The materials and processes used to generate the integrated neural interface device were found to yield a robust and reliable integrated package.

The case for a generic implant processor

A more structured and streamlined design of implants is nowadays possible. In this paper we focus on implant processors located in the heart of implantable systems. We present a real and representative biomedical-application scenario where such a new processor can be employed. Based on a suitably selected processor simulator, various operational aspects of the application are being monitored. Findings on performance, cache behavior, branch prediction, power consumption, energy expenditure and instruction mixes are presented and analyzed. The suitability of such an implant processor and directions for future work are given.

Battery-operated high-bandwidth multi-channel wireless neural recording system using 802.11b

This paper reports the design of a battery-operated, high bandwidth, multi-channel wireless medical telemetry system. The system is capable of transmitting 2.3 Mbps of raw streaming data using the IEEE 802.11b protocol. In a typical application, the system was used to collect data from micro-wire electrodes implanted in the ventral striatum of an awake and behaving rat. The complete system weighs 87 g (without battery) and consumes 2.7 W.

A TinyOS-Enabled MICA2-Based Wireless Neural Interface

Existing approaches used to develop compact low-power multichannel wireless neural recording systems range from creating custom-integrated circuits to assembling commercial-off-the-shelf (COTS) PC-based components. Custom-integrated-circuit designs yield extremely compact and low-power devices at the expense of high development and upgrade costs and turn-around times, while assembling COTS-PC-technology yields high performance at the expense of large system size and increased power consumption. To achieve a balance between implementing an ultra-compact custom-fabricated neural transceiver and assembling COTS-PC-technology, an overlay of a neural interface upon the TinyOS-based MICA2 platform is described. The system amplifies, digitally encodes, and transmits neural signals real-time at a rate of 9.6 kbps, while consuming less than 66 mW of power. The neural signals are received and forwarded to a client PC over a serial connection. This data rate can be divided for recording on up to 6 channels, with a resolution of 8 bits/sample. This work demonstrates the strengths and limitations of the TinyOS-based sensor technology as a foundation for chronic remote biological monitoring applications and, thus, provides an opportunity to create a system that can leverage from the frequent networking and communications advancements being made by the global TinyOS-development community.

Wireless multichannel biopotential recording using an integrated FM telemetry circuit

This paper presents a four-channel telemetric microsystem featuring on-chip alternating current amplification, direct current baseline stabilization, clock generation, time-division multiplexing, and wireless frequency-modulation transmission of microvolt- and millivolt-range input biopotentials in the very high frequency band of 94-98 MHz over a distance of approximately 0.5 m. It consists of a 4.84-mm2 integrated circuit, fabricated using a 1.5-microm double-poly double-metal n-well standard complementary metal-oxide semiconductor process, interfaced with only three off-chip components on a custom-designed printed-circuit board that measures 1.7 x 1.2 x 0.16 cm3, and weighs 1.1 g including two miniature 1.5-V batteries. We characterize the microsystem performance, operating in a truly wireless fashion in single-channel and multichannel operation modes, via extensive benchtop and in vitro tests in saline utilizing two different micromachined neural recording microelectrodes, while dissipating approximately 2.2 mW from a 3-V power supply. Moreover, we demonstrate successful wireless in vivo recording of spontaneous neural activity at 96.2 MHz from the auditory cortex of an awake marmoset monkey at several transmission distances ranging from 10 to 50 cm with signal-to-noise ratios in the range of 8.4-9.5 dB.

A 1.48-mW low-phase-noise analog frequency modulator for wireless biotelemetry

This paper presents a low-phase-noise, hybrid LC-tank, analog frequency modulator for wireless biotelemetry employing on-chip NMOS varactors in the inversion region as the frequency tuning element. We demonstrate that a correct estimate for the destination signal-to-noise ratio, which quantifies the quality of the wirelessly received signal in a frequency-modulated biotelemetry system, is only achieved after taking into account the large-signal oscillation effect on the tank varactor. A prototype chip is fabricated using AMI 1.5-microm double-poly double-metal n-well CMOS process, and exhibits a measured gain factor of 1.21 MHz/V in the mid-range of the tuning voltage and a phase noise of -88.6 dBc/Hz at 10-kHz offset from the 95.1-MHz carrier while dissipating 1.48 mW from a 3 V power supply leading to a figure of merit (FOM) of -166.5 dBc/Hz. The VCO is successfully interfaced with a penetrating silicon microelectrode with 700 microm2 iridium recording sites for wireless in vitro recording of a 50 Hz simulated normal sinus rhythm signal from saline over a distance of approximately 0.25 m. Given a typical gain of approximately 40 dB for fully integrated front-end bioamplifiers, a wireless recording microsystem employing this VCO would be capable of detecting input biopotentials down to the submilivolt range.