A Multimodal Neural Activity Readout Integrated Circuit for Recording Fluorescence and Electrical Signals

Monitoring the electrical neural signals is an important method for understanding the neuronal mechanism. In particular, in order to perform a cell-type-specific study, it is necessary to observe the concentration of calcium ions using fluorescent indicators in addition to measuring the electrical neural signal. This paper presents a multimodal multichannel neural activity readout integrated circuit that can perform not only electrical neural recording but also fluorescence recording of neural activity for the cell-type-specific study of heterogeneous neuronal cell populations. For monitoring the calcium ions, the photodiode generates the current according to the fluorescence expressed by the reaction between the genetically encoded calcium indicators and calcium ions. The time-based fluorescence recording circuit then records the photodiode current. The electrical neural signal captured by the microelectrode is recorded through the low-noise amplifier, variable gain amplifier, and analog-to-digital converter. The proposed integrated circuit is fabricated in a 1-poly 6-metal (1P6M) 0.18- ??m CMOS process. The fluorescence recording circuit achieves a recording range of 81 dB (75 pA to 860 nA) and consumes a power of 724 nW/channel. The electrical recording circuit achieves an input-referred noise of 2.7 ??Vrms over the bandwidth of 10 kHz, while consuming the power of 4.9 ??W /channel. The functionality of the proposed circuits is verified through the in vivo and in vitro experiments. Compared to the conventional neuroscience tools, which consist of bulky off-chip components, this neural interface is implemented in a compact size to perform multimodal neural recording while consuming low power

Doctor of Philosophy

dissertationThis dissertation describes the use of cortical surface potentials, recorded with dense grids of microelectrodes, for brain-computer interfaces (BCIs). The work presented herein is an in-depth treatment of a broad and interdisciplinary topic, covering issues from electronics to electrodes, signals, and applications. Within the scope of this dissertation are several significant contributions. First, this work was the first to demonstrate that speech and arm movements could be decoded from surface local field potentials (LFPs) recorded in human subjects. Using surface LFPs recorded over face-motor cortex and Wernickes area, 150 trials comprising vocalized articulations of ten different words were classified on a trial-by-trial basis with 86% accuracy. Surface LFPs recorded over the hand and arm area of motor cortex were used to decode continuous hand movements, with correlation of 0.54 between the actual and predicted position over 70 seconds of movement. Second, this work is the first to make a detailed comparison of cortical field potentials recorded intracortically with microelectrodes and at the cortical surface with both micro- and macroelectrodes. Whereas coherence in macroelectrocorticography (ECoG) decayed to half its maximum at 5.1 mm separation in high frequencies, spatial constants of micro-ECoG signals were 530-700 ?m-much closer to the 110-160 ?m calculated for intracortical field potentials than to the macro-ECoG. These findings confirm that cortical surface potentials contain millimeter-scale dynamics. Moreover, these fine spatiotemporal features were important for the performance of speech and arm movement decoding. In addition to contributions in the areas of signals and applications, this dissertation includes a full characterization of the microelectrodes as well as collaborative work in which a custom, low-power microcontroller, with features optimized for biomedical implants, was taped out, fabricated in 65 nm CMOS technology, and tested. A new instruction was implemented in this microcontroller which reduced energy consumption when moving large amounts of data into memory by as much as 44%. This dissertation represents a comprehensive investigation of surface LFPs as an interfacing medium between man and machine. The nature of this work, in both the breadth of topics and depth of interdisciplinary effort, demonstrates an important and developing branch of engineering

A Low-Power Wireless Multichannel Microsystem for Reliable Neural Recording.

This thesis reports on the development of a reliable, single-chip, multichannel wireless biotelemetry microsystem intended for extracellular neural recording from awake, mobile, and small animal models. The inherently conflicting requirements of low power and reliability are addressed in the proposed microsystem at architectural and circuit levels. Through employing the preliminary microsystems in various in-vivo experiments, the system requirements for reliable neural recording are identified and addressed at architectural level through the analytical tool: signal path co-optimization. The 2.85mm×3.84mm, mixed-signal ASIC integrates a low-noise front-end, programmable digital controller, an RF modulator, and an RF power amplifier (PA) at the ISM band of 433MHz on a single-chip; and is fabricated using a 0.5µm double-poly triple-metal n-well standard CMOS process. The proposed microsystem, incorporating the ASIC, is a 9-channel (8-neural, 1-audio) user programmable reliable wireless neural telemetry microsystem with a weight of 2.2g (including two 1.5V batteries) and size of 2.2×1.1×0.5cm3. The electrical characteristics of this microsystem are extensively characterized via benchtop tests. The transmitter consumes 5mW and has a measured total input referred voltage noise of 4.74µVrms, 6.47µVrms, and 8.27µVrms at transmission distances of 3m, 10m, and 20m, respectively. The measured inter-channel crosstalk is less than 3.5% and battery life is about an hour. To compare the wireless neural telemetry systems, a figure of merit (FoM) is defined as the reciprocal of the power spent on broadcasting one channel over one meter distance. The proposed microsystem’s FoM is an order of magnitude larger compared to all other research and commercial systems. The proposed biotelemetry system has been successfully used in two in-vivo neural recording experiments: i) from a freely roaming South-American cockroach, and ii) from an awake and mobile rat.PhDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91542/1/aborna_1.pd

Integrated Electronics to Control and Readout Electrochemical Biosensors for Implantable Applications

Biosensors can effectively be used to monitor multiple metabolites such as glucose, lactate, ATP and drugs in the human body. Continuous monitoring of these metabolites is essential for patients with chronic or critical conditions. Moreover, this can be used to tune the dosage of a drug for each individual patient, in order to achieve personalized therapy. Implantable medical devices (IMDs) based on biosensors are emerging as a valid alternative for blood tests in laboratories. They can provide continuous monitoring while reduce the test costs. The potentiostat plays a fundamental role in modern biosensors. A potentiostat is an electronic device that controls the electrochemical cell, using three electrodes, and runs the electrochemical measurement. In particular the IMDs require a low-power, fully-integrated, and autonomous potentiostats to control and readout the biosensors. This thesis describes two integrated circuits (ICs) to control and readout multi-target biosensors: LOPHIC and ARIC. They enable chronoamperometry and cyclic voltammetrymeasurements and consume sub-mW power. The design, implementation, characterisation, and validation with biosensors are presented for each IC. To support the calibration of the biosensors with environmental parameters, ARIC includes circuitry to measure the pHand temperature of the analyte through an Iridiumoxide pH sensor and an off-chip resistor-temperature detector (RTD). In particular, novel circuits to convert resistor value into digital are designed for RTD readout. ARIC is integrated into two IMDs aimed for health-care monitoring and personalized therapy. The control and readout of the embedded sensor arrays have been successfully achieved, thanks to ARIC, and validated for glucose and paracetamol measurements while it is remotely powered through an inductive link. To ensure the security and privacy of IMDs, a lightweight cryptographic system (LCS) is presented. This is the first ASIC implementation of a cryptosystem for IMDs, and is integrated into ARIC. The resulting system provides a unique and fundamental capability by immediately encrypting and signing the sensor data upon its creation within the body. Nano-structures such as Carbon nanotubes have been widely used to improve the sensitivity of the biosensors. However, in most of the cases, they introduce more noise into the measurements and produce a large background current. In this thesis the noise of the sensors incorporating CNTs is studied for the first time. The effect of CNTs as well as sensor geometry on the signal to noise ratio of the sensors is investigated experimentally. To remove the background current of the sensors, a differential readout scheme has been proposed. In particular, a novel differential readout IC is designed and implemented that measures inputcurrents within a wide dynamic range and produces a digital output that corresponds to the -informative- redox current of the biosensor