Recent Advances in Neural Recording Microsystems

The accelerating pace of research in neuroscience has created a considerable demand for neural interfacing microsystems capable of monitoring the activity of large groups of neurons. These emerging tools have revealed a tremendous potential for the advancement of knowledge in brain research and for the development of useful clinical applications. They can extract the relevant control signals directly from the brain enabling individuals with severe disabilities to communicate their intentions to other devices, like computers or various prostheses. Such microsystems are self-contained devices composed of a neural probe attached with an integrated circuit for extracting neural signals from multiple channels, and transferring the data outside the body. The greatest challenge facing development of such emerging devices into viable clinical systems involves addressing their small form factor and low-power consumption constraints, while providing superior resolution. In this paper, we survey the recent progress in the design and the implementation of multi-channel neural recording Microsystems, with particular emphasis on the design of recording and telemetry electronics. An overview of the numerous neural signal modalities is given and the existing microsystem topologies are covered. We present energy-efficient sensory circuits to retrieve weak signals from neural probes and we compare them. We cover data management and smart power scheduling approaches, and we review advances in low-power telemetry. Finally, we conclude by summarizing the remaining challenges and by highlighting the emerging trends in the field

A 129NW Neural Amplifier and Gm-C Filter for EEG Using gm/ID Methodology

This Thesis presents a low-power analog front-end amplifier and Gm-C filter for biomedical sensing applications, specifically for Electroencephalogram (EEG) use. The proposed neural amplifier uses a supply voltage of 1.8V, it has a mid-band gain of 40.75dB, and consumes a total current of 71.82nA, for a total dissipated power of 129.276nW. Also presented is the design of a 3rd order Butterworth Low Pass Gm-C Filter which makes use of 14.7nS transconductors; the proposed filter has a pass band suitable for EEG recording use (1-100Hz). The amplifier and filter utilize current sources without bias resistances which provide 56nA and (1.154nA x 5) respectively. The proposed neural amplifier occupies a chip area of 0.275mm2 in a 0.3μm TSMC process. Simulation of the schematic and extracted chip layout is presented, along with a comparison of similar published works. Finally, a projected power consumption calculation for a multichannel system based on this system is offered

Flexible, polarization-diverse UWB antennas for implantable neural recording systems

Implanted antennas for implant-to-air data communications must be composed of material compatible with biological tissues. We design single and dual-polarization antennas for wireless ultra-wideband neural recording systems using an inhomogeneous multi-layer model of the human head. Antennas made from flexible materials are more easily adapted to implantation; we investigate both flexible and rigid materials and examine performance trade-offs. The proposed antennas are designed to operate in a frequency range of 2-11 GHz (having S11 below -10 dB) covering both the 2.45 GHz (ISM) band and the 3.1-10.6 GHz UWB band. Measurements confirm simulation results showing flexible antennas have little performance degradation due to bending effects (in terms of impedance matching). Our miniaturized flexible antennas are 12 mm×12 mm and 10 mm×9 mm for single- and dual-polarizations, respectively. Finally, a comparison is made of four implantable antennas covering the 2-11 GHz range: 1) rigid, single polarization, 2) rigid, dual polarization, 3) flexible, single polarization and 4) flexible, dual polarization. In all cases a rigid antenna is used outside the body, with an appropriate polarization. Several advantages were confirmed for dual polarization antennas: 1) smaller size, 2) lower sensitivity to angular misalignments, and 3) higher fidelity

System level design of a full-duplex wireless transceiver for brain-machine interfaces

We propose a new wireless communication architecture for implanted systems that simultaneously stimulates neurons and record neural responses. This architecture can support large numbers of electrodes (>500), providing 100 Mb/s for the downlink of stimulation signals, and gigabits per second for the uplink of neural recordings. We propose a full-duplex transceiver architecture that shares one antenna for both the ultrawideband (UWB) and the 2.45-GHz industrial, scientific, and medical band. A new pulse shaper is used for the gigabits per second uplink to simplify the transceiver design, while supporting several modulation formats with high data rates. To validate our system-level design for brain-machine interfaces, we present an ex-vivo experimental demonstration of the architecture. While the system design is for an integrated solution, the proof-of-concept demonstration uses discrete components. Good bit error rate performance over a biological channel at 0.5-, 1-, and 2-Gb/s data rates for uplink telemetry (UWB) and 100 Mb/s for downlink telemetry (2.45-GHz band) are achieved

A functional model and simulation of spinal motor pools and intrafascicular recordings of motoneuron activity in peripheral nerve

Decoding motor intent from recorded neural signals is essential for the development of effective neural-controlled prostheses. To facilitate the development of online decoding algorithms we have developed a software platform to simulate neural motor signals recorded with peripheral nerve electrodes, such as longitudinal intrafascicular electrodes (LIFEs). The simulator uses stored motor intent signals to drive a pool of simulated motoneurons with various spike shapes, recruitment characteristics, and firing frequencies. Each electrode records a weighted sum of a subset of simulated motoneuron activity patterns. As designed, the simulator facilitates development of a suite of test scenarios that would not be possible with actual data sets because, unlike with actual recordings, in the simulator the individual contributions to the simulated composite recordings are known and can be methodically varied across a set of simulation runs. In this manner, the simulation tool is suitable for iterative development of real-time decoding algorithms prior to definitive evaluation in amputee subjects with implanted electrodes. The simulation tool was used to produce data sets that demonstrate its ability to capture some features of neural recordings that pose challenges for decoding algorithms

Using the piezoelectric backscatter signal for remote sensing of neural signals

In recent studies, various methods to sense neural signals are used and new methods for remote sensing of neural signals are being developed. However, there are still major difficulties in building long-term implantable neural interface systems that can reliably record neural activity and serve as the basis of brain-machine interfaces (BMI). Therefore, this research is conducted to design a remote neural sensing system that is based on modulation of the backscatter signal from a piezoelectric element by the neural signals. The hypothesis is that if the neural signal is detected with a simple amplifier and the output of this amplifier is connected in parallel to a piezoelectric element, the backscattered signal from the piezoelectric element should be modulated by the neural signal amplitudes. To this end, the echo signal from the piezoelectric element is analyzed and the effect of a load resistor is demonstrated. And then, an electronic circuit to implement the modulation function is simulated on the computer and constructed. The experimental results support the main hypothesis of the project

A functional model and simulation of spinal motor pools and intrafascicular recordings of motoneuron activity in peripheral nerve

abstract: Decoding motor intent from recorded neural signals is essential for the development of effective neural-controlled prostheses. To facilitate the development of online decoding algorithms we have developed a software platform to simulate neural motor signals recorded with peripheral nerve electrodes, such as longitudinal intrafascicular electrodes (LIFEs). The simulator uses stored motor intent signals to drive a pool of simulated motoneurons with various spike shapes, recruitment characteristics, and firing frequencies. Each electrode records a weighted sum of a subset of simulated motoneuron activity patterns. As designed, the simulator facilitates development of a suite of test scenarios that would not be possible with actual data sets because, unlike with actual recordings, in the simulator the individual contributions to the simulated composite recordings are known and can be methodically varied across a set of simulation runs. In this manner, the simulation tool is suitable for iterative development of real-time decoding algorithms prior to definitive evaluation in amputee subjects with implanted electrodes. The simulation tool was used to produce data sets that demonstrate its ability to capture some features of neural recordings that pose challenges for decoding algorithms

Low-noise Amplifier for Neural Recording

With a combination of engineering approaches and neurophysiological knowledge of the central nervous system, a new generation of medical devices is being developed to link groups of neurons with microelectronic systems. By doing this, researchers are acquiring fundamental knowledge of the mechanisms of disease and innovating treatments for disabilities in patients who have a failure of communication along neural pathways. A low-noise and low-power analog front-end circuit is one of the primary requirements for neural recording. The main function for the front-end amplifier is to provide gain over the bandwidth of neural signals and to reject undesired frequency components. The chip developed in this thesis is a field-programmable analog front-end amplifier consisting of 16 programmable channels with tunable frequency response. A capacitively coupled two-stage amplifier is used. The first-stage amplifier is a Low-Noise Amplifier (LNA), as it directly interfaces with the neural recording micro-electrodes; the second stage is a high gain and high swing amplifier. A MOS resistor in the feedback path is used to get tunable low-cut-off frequency and reject the dc offset voltage. Our design builds upon previous recording chips designed by two former graduate stu- dents in our lab. In our design, the circuits are optimized for low noise. Our simulations show the recording channel has a gain of 77.9 dB and input-referred noise of 6.95 µV rms(Root-Mean-Square voltage) over 750 Hz to 6.9 kHz. The chip is fabricated in AMS 0.35 µm CMOS technology for a total die area of 3 x 3 mm 2 and Total Power Dissipation (TPD) of 2.9 mW. To verify the functionality and adherence to the design specifications it will be tested on Printed-Circuit-Board

Diseño de un amplificador chopper de señales neuronales

En el presente trabajo de tesis se diseña un amplificador para ser utilizado como parte de un sistema de adquisición de señales neuronales. La topología elegida para el desarrollo fue la de cascodo plegado de una sola salida (single ended folded cascode), ubicando los moduladores chopper de manera que no haya limitación debido al ancho de banda. Debido a que este trabajo está enfocado a dispositivos implantables, se requiere de un bajo consumo de potencia, así como una pequeña área ocupada. A estos dos requerimientos se suma el de ruido, el cual es de gran importancia al ser esta la primera etapa del sistema. Se utilizó el software CADENCE para realizar distintas simulaciones que comprueban el correcto análisis realizado. Los resultados más importantes previo a la aplicación de la técnica chopper son: el ruido referido a la entrada de 2.92Vrms, con una potencia consumida de 36.78uW utilizando una fuente de alimentación de 3.3V, la ganancia de lazo abierto es de 102.1dB y la ganancia de lazo cerrado es de 45.88dB con un ancho de banda de 7.96kHz. El área ocupada por el circuito es de 0.0073mm2.Tesi