A 64-channel inductively-powered neural recording sensor array

This paper reports a 64-channel inductively powered neural recording sensor array. Neural signals are acquired, filtered, digitized and compressed in the channels. Additionally, each channel implements a local auto-calibration mechanism which configures the transfer characteristics of the recording site. The system has two operation modes; in one case the information captured by the channels is sent as uncompressed raw data; in the other, feature vectors extracted from the detected neural spikes are transmitted. Data streams coming from the channels are serialized by an embedded digital processor and transferred to the outside by means of the same inductive link used for powering the system. Simulation results show that the power consumption of the complete system is 377μW.Ministerio de Ciencia e Innovación TEC2009-0844

Multiplexed, High Density Electrophysiology with Nanofabricated Neural Probes

Extracellular electrode arrays can reveal the neuronal network correlates of behavior with single-cell, single-spike, and sub-millisecond resolution. However, implantable electrodes are inherently invasive, and efforts to scale up the number and density of recording sites must compromise on device size in order to connect the electrodes. Here, we report on silicon-based neural probes employing nanofabricated, high-density electrical leads. Furthermore, we address the challenge of reading out multichannel data with an application-specific integrated circuit (ASIC) performing signal amplification, band-pass filtering, and multiplexing functions. We demonstrate high spatial resolution extracellular measurements with a fully integrated, low noise 64-channel system weighing just 330 mg. The on-chip multiplexers make possible recordings with substantially fewer external wires than the number of input channels. By combining nanofabricated probes with ASICs we have implemented a system for performing large-scale, high-density electrophysiology in small, freely behaving animals that is both minimally invasive and highly scalable

Spatiotemporal Sparse Bayesian Learning with Applications to Compressed Sensing of Multichannel Physiological Signals

Energy consumption is an important issue in continuous wireless telemonitoring of physiological signals. Compressed sensing (CS) is a promising framework to address it, due to its energy-efficient data compression procedure. However, most CS algorithms have difficulty in data recovery due to non-sparsity characteristic of many physiological signals. Block sparse Bayesian learning (BSBL) is an effective approach to recover such signals with satisfactory recovery quality. However, it is time-consuming in recovering multichannel signals, since its computational load almost linearly increases with the number of channels. This work proposes a spatiotemporal sparse Bayesian learning algorithm to recover multichannel signals simultaneously. It not only exploits temporal correlation within each channel signal, but also exploits inter-channel correlation among different channel signals. Furthermore, its computational load is not significantly affected by the number of channels. The proposed algorithm was applied to brain computer interface (BCI) and EEG-based driver's drowsiness estimation. Results showed that the algorithm had both better recovery performance and much higher speed than BSBL. Particularly, the proposed algorithm ensured that the BCI classification and the drowsiness estimation had little degradation even when data were compressed by 80%, making it very suitable for continuous wireless telemonitoring of multichannel signals.Comment: Codes are available at: https://sites.google.com/site/researchbyzhang/stsb

Communication channel analysis and real time compressed sensing for high density neural recording devices

Next generation neural recording and Brain- Machine Interface (BMI) devices call for high density or distributed systems with more than 1000 recording sites. As the recording site density grows, the device generates data on the scale of several hundred megabits per second (Mbps). Transmitting such large amounts of data induces significant power consumption and heat dissipation for the implanted electronics. Facing these constraints, efficient on-chip compression techniques become essential to the reduction of implanted systems power consumption. This paper analyzes the communication channel constraints for high density neural recording devices. This paper then quantifies the improvement on communication channel using efficient on-chip compression methods. Finally, This paper describes a Compressed Sensing (CS) based system that can reduce the data rate by > 10x times while using power on the order of a few hundred nW per recording channel

A wireless implantable multichannel digital neural recording system for a micromachined sieve electrode

This paper reports the development of an implantable, fully integrated, multichannel peripheral neural recording system, which is powered and controlled using an RF telemetry link, The system allows recording of +/-500 mu V neural signals from axons regenerated through a micromachined silicon sieve electrode, These signals are amplified using on-chip 100 Hz to 3.1 kHz bandlimited amplifiers, multiplexed, and digitized with a low-power (<2 mW), moderate speed (8 mu s/b) current-mode 8-b analog-to-digital converter (ADC), The digitized signal is transmitted to the outside world using a passive RF telemetry link, The circuit is implemented using a bipolar CMOS process, The signal processing CMOS circuitry dissipates only 10 mW of power from a 5-V supply while operating at 2 MHz and consumes 4 x 4 mm(2) of area, The overall circuit including the RF interface circuitry contains over 5000 transistors, dissipates 90 mW of power, and consumes 4 x 6 mm(2) of area

A new modulation technique for high data rate low power UWB wireless optical communication in implantable biotelemetry systems

We report on the development of a novel modulation technique for UWB wireless optical communication systems for application in a transcutaneous biotelemetry. The solution, based on the generation of short laser pulses, allows for a high data rate link whilst achieving a significant power reduction (energy per bit) compared to the state-of-the-art. These features make this particularly suitable for emerging biomedical applications such as implantable neural/biosensor systems. The relatively simple architecture consists of a transmitter and receiver that can be integrated in a standard CMOS technology in a compact Silicon footprint. These parts include circuits for bias and drive current generation, conditioning and processing, optimised for low-volt age/low-power operation. Preliminary experimental findings validate the new paradigm and show good agreement with expected results. The complete system achieves a BER less than 10-7, with maximum data rate of 125Mbps and estimated total power consumption of less than 3mW

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 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.Ph.D.Electrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91542/1/aborna_1.pd

Advances in Microelectronics for Implantable Medical Devices

Implantable medical devices provide therapy to treat numerous health conditions as well as monitoring and diagnosis. Over the years, the development of these devices has seen remarkable progress thanks to tremendous advances in microelectronics, electrode technology, packaging and signal processing techniques. Many of today’s implantable devices use wireless technology to supply power and provide communication. There are many challenges when creating an implantable device. Issues such as reliable and fast bidirectional data communication, efficient power delivery to the implantable circuits, low noise and low power for the recording part of the system, and delivery of safe stimulation to avoid tissue and electrode damage are some of the challenges faced by the microelectronics circuit designer. This paper provides a review of advances in microelectronics over the last decade or so for implantable medical devices and systems. The focus is on neural recording and stimulation circuits suitable for fabrication in modern silicon process technologies and biotelemetry methods for power and data transfer, with particular emphasis on methods employing radio frequency inductive coupling. The paper concludes by highlighting some of the issues that will drive future research in the field