We present a fully differential 128-channel integrated neural interface. It consists of an array of 8 X 16 low-power low-noise signal-recording and generation circuits for electrical neural activity monitoring and stimulation, respectively. The recording channel has two stages of signal amplification and conditioning with and a fully differential 8-b column-parallel successive approximation (SAR) analog-to-digital converter (ADC). The total measured power consumption of each recording channel, including the SAR ADC, is 15.5 ?W. The measured input-referred noise is 6.08 ? Vrms over a 5-kHz bandwidth, resulting in a noise efficiency factor of 5.6. The stimulation channel performs monophasic or biphasic voltage-mode stimulation, with a maximum stimulation current of 5 mA and a quiescent power dissipation of 51.5 ?W. The design is implemented in 0.35-?m complementary metal-oxide semiconductor technology with the channel pitch of 200 ?m for a total die size of 3.4 mm × 2.5 mm and a total power consumption of 9.33 mW. The neural interface was validated in in vitro recording of a low-Mg2+/high-K+ epileptic seizure model in an intact hippocampus of a mouse.

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The 128-Channel Fully Differential Digital Integrated Neural Recording and Stimulation Interface

A patient controls the delivery of therapy through volitional inputs that are detected by a biosignal within the brain. The volitional patient input may be directed towards performing a specific physical or mental activity, such as moving a muscle or performing a mathematical calculation. In one embodiment, a biosignal detection module monitors an electroencephalogram (EEG) signal from within the brain of the patient and determines whether the EEG signal includes the biosignal. In one embodiment, the biosignal detection module analyzes one or more frequency components of the EEG signal. In this manner, the patient may adjust therapy delivery by providing a volitional input that is detected by brain signals, wherein the volitional input may not require the interaction with another device, thereby eliminating the need for an external programmer to adjust therapy delivery. Example therapies include electrical stimulation, drug delivery, and delivery of sensory cues.

Patient directed therapy control

Optogenetics, the ability to use light to activate and silence specific neuron types within neural networks in vivo and in vitro, is revolutionizing neuroscientists' capacity to understand how defined neural circuit elements contribute to normal and pathological brain functions. Typically, awake behaving experiments are conducted by inserting an optical fiber into the brain, tethered to a remote laser, or by utilizing an implanted light-emitting diode (LED), tethered to a remote power source. A fully wireless system would enable chronic or longitudinal experiments where long duration tethering is impractical, and would also support high-throughput experimentation. However, the high power requirements of light sources (LEDs, lasers), especially in the context of the extended illumination periods often desired in experiments, precludes battery-powered approaches from being widely applicable. We have developed a headborne device weighing 2 g capable of wirelessly receiving power using a resonant RF power link and storing the energy in an adaptive supercapacitor circuit, which can algorithmically control one or more headborne LEDs via a microcontroller. The device can deliver approximately 2 W of power to the LEDs in steady state, and 4.3 W in bursts. We also present an optional radio transceiver module (1 g) which, when added to the base headborne device, enables real-time updating of light delivery protocols; dozens of devices can be controlled simultaneously from one computer. We demonstrate use of the technology to wirelessly drive cortical control of movement in mice. These devices may serve as prototypes for clinical ultra-precise neural prosthetics that use light as the modality of biological control.

/pdf/a-wirelessly-powered-and-controlled-device-for-optical-3tgc0scmnq.pdf

A wirelessly powered and controlled device for optical neural control of freely-behaving animals

We have developed an implantable fuel cell that generates power through glucose oxidation, producing steady-state power and up to peak power. The fuel cell is manufactured using a novel approach, employing semiconductor fabrication techniques, and is therefore well suited for manufacture together with integrated circuits on a single silicon wafer. Thus, it can help enable implantable microelectronic systems with long-lifetime power sources that harvest energy from their surrounds. The fuel reactions are mediated by robust, solid state catalysts. Glucose is oxidized at the nanostructured surface of an activated platinum anode. Oxygen is reduced to water at the surface of a self-assembled network of single-walled carbon nanotubes, embedded in a Nafion film that forms the cathode and is exposed to the biological environment. The catalytic electrodes are separated by a Nafion membrane. The availability of fuel cell reactants, oxygen and glucose, only as a mixture in the physiologic environment, has traditionally posed a design challenge: Net current production requires oxidation and reduction to occur separately and selectively at the anode and cathode, respectively, to prevent electrochemical short circuits. Our fuel cell is configured in a half-open geometry that shields the anode while exposing the cathode, resulting in an oxygen gradient that strongly favors oxygen reduction at the cathode. Glucose reaches the shielded anode by diffusing through the nanotube mesh, which does not catalyze glucose oxidation, and the Nafion layers, which are permeable to small neutral and cationic species. We demonstrate computationally that the natural recirculation of cerebrospinal fluid around the human brain theoretically permits glucose energy harvesting at a rate on the order of at least 1 mW with no adverse physiologic effects. Low-power brain–machine interfaces can thus potentially benefit from having their implanted units powered or recharged by glucose fuel cells.

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A Glucose Fuel Cell for Implantable Brain–Machine Interfaces

Part I. Foundations: Ten chapters lay a foundation in device physics, noise, and feedback systems including nano scales in a highly original fashion, emphasizing intuitive thinking. This foundation is important in designing and analyzing ultra-low-power systems in both electronics and biology Part II. Low-Power Analog and Biomedical Circuits: Five chapters present building-block circuits that are useful for ultra-low-power biomedical electronics and analog electronic systems in general Part III. Low-Power RF and Energy-Harvesting Circuits for Biomedical Systems: Three chapters provide an in-depth description of energy-efficient power and data radio-frequency (RF) links that are fundamental to biomedical systems Part IV. Biomedical Electronic Systems: Two chapters provide an in-depth look at ultra-low-power implantable electronics and ultra-low-power noninvasive electronics for biomedical applications, respectively. Case studies for cochlear implants for the deaf, brain implants for the blind and paralyzed, wearable cardiac devices, and biomolecular sensing are provided Part V. Principles for Ultra-Low-Power Analog and Digital Design: Two chapters discuss principles for ultra-low-power digital design and ultra-low-power analog and mixed-signal design, respectively. The chapters identify ten fundamental principles that are common in both biology and electronics, analog and digital design Part VI. Bio-Inspired Systems: A chapter on neuromorphic electronics discusses electronics inspired by neurobiology followed by a chapter that discusses a novel form of electronics termed Cytomorphic Electronics, electronics inspired by cell biology. These chapters discuss applications of bio-inspired systems to engineering and medicine, deep connections between chemistry and electronics, and provide a unifying viewpoint of ultra-low-power design in biology and in electronics Part VII. Energy Sources: A chapter on batteries and electrochemistry discusses how batteries work from a unique circuit viewpoint. The last chapter discusses energy harvesting in biomedical systems at portable scales (vibration and body heat) and at larger scales (low-power cars and solar cells). Principles of low-power design are shown to extend from small scales in electronics to larger scales and to non-electrical systems. This book reveals the deep connections between energy use and energy generation, vital for sustainable energy systems of the future.

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Ultra Low Power Bioelectronics: Fundamentals, Biomedical Applications, and Bio-Inspired Systems

In this paper, we present a novel energy-efficient electrode stimulator. Our stimulator uses inductive storage and recycling of energy in a dynamic power supply. This supply drives an electrode in an adiabatic fashion such that energy consumption is minimized. It also utilizes a shunt current-sensor to monitor and regulate the current through the electrode via feedback, thus enabling flexible and safe stimulation. Since there are no explicit current sources or current limiters, wasteful energy dissipation across such elements is naturally avoided. The dynamic power supply allows efficient transfer of energy both to and from the electrode and is based on a DC-DC converter topology that we use in a bidirectional fashion in forward-buck or reverse-boost modes. In an exemplary electrode implementation intended for neural stimulation, we show how the stimulator combines the efficiency of voltage control and the safety and accuracy of current control in a single low-power integrated-circuit built in a standard .35 μm CMOS process. This stimulator achieves a 2x-3x reduction in energy consumption as compared to a conventional current-source-based stimulator operating from a fixed power supply. We perform a theoretical analysis of the energy efficiency that is in accord with experimental measurements. This theoretical analysis reveals that further improvements in energy efficiency may be achievable with better implementations in the future. Our electrode stimulator could be widely useful for neural, cardiac, retinal, cochlear, muscular and other biomedical implants where low power operation is important.

An Energy-Efficient, Adiabatic Electrode Stimulator With Inductive Energy Recycling and Feedback Current Regulation

This paper presents work on ultra-low-power circuits for brain–machine interfaces with applications for paralysis prosthetics, stroke, Parkinson's disease, epilepsy, prosthetics for the blind, and experimental neuroscience systems. The circuits include a micropower neural amplifier with adaptive power biasing for use in multi-electrode arrays; an analog linear decoding and learning architecture for data compression; low-power radio-frequency (RF) impedance-modulation circuits for data telemetry that minimize power consumption of implanted systems in the body; a wireless link for efficient power transfer; mixed-signal system integration for efficiency, robustness, and programmability; and circuits for wireless stimulation of neurons with power-conserving sleep modes and awake modes. Experimental results from chips that have stimulated and recorded from neurons in the zebra finch brain and results from RF power-link, RF data-link, electrode-recording and electrode-stimulating systems are presented. Simulations of analog learning circuits that have successfully decoded prerecorded neural signals from a monkey brain are also presented.

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Low-Power Circuits for Brain–Machine Interfaces

We describe an approximately-PTAT CMOS current reference circuit that is useful for large analog systems. An innovative capacitively-coupled startup circuit that draws no static power is also presented. Experimental results from 0.5 /spl mu/m and 0.18 /spl mu/m implementations are shown. Both references are cascoded and use no resistors. The 0.18 /spl mu/m version operates down to V/sub DD/ = 0.5V.

Fast startup CMOS current references

We introduce a novel wireless, low-power neural stimulation system for use in freely behaving animals. The system consists of an external transmitter and a miniature, implantable wireless receiver-stimulator. The implant uses a custom integrated chip to deliver biphasic current pulses to four addressable bipolar electrodes at 32 selectable current levels (10 microA to 1 mA). To achieve maximal battery life, the chip enters a sleep mode when not needed and can be awakened remotely when required. To test our device, we implanted bipolar stimulating electrodes into the songbird motor nucleus HVC (formerly called the high vocal center) of zebra finches. Single-neuron recordings revealed that wireless stimulation of HVC led to a strong increase of spiking activity in its downstream target, the robust nucleus of the arcopallium. When we used this device to deliver biphasic pulses of current randomly during singing, singing activity was prematurely terminated in all birds tested. Thus our device is highly effective for remotely modulating a neural circuit and its corresponding behavior in an untethered, freely behaving animal.

https://www.physiology.org/doi/pdf/10.1152/jn.00017.2009

Wireless Neural Stimulation in Freely Behaving Small Animals

This paper presents work on ultra-low-power circuits for brain-machine interfaces with applications for paralysis prosthetics, prosthetics for the blind, and experimental neuroscience systems. The circuits include a micropower neural amplifier with adaptive power biasing for use in multi-electrode arrays; an analog linear decoding and learning architecture for data compression; radio-frequency (RF) impedance modulation for low-power data telemetry; a wireless link for efficient power transfer; mixed-signal system integration for efficiency, robustness, and programmability; and circuits for wireless stimulation of neurons. Experimental results from chips that have recorded from and stimulated neurons in the zebra-finch brain and from RF power-link systems are presented. Circuit simulations that have successfully processed prerecorded data from a monkey brain and from an RF data telemetry system are also presented.

/pdf/low-power-circuits-for-brain-machine-interfaces-qflgp8x5vd.pdf

Scott K. Arfin

Papers

An Energy-Efficient, Adiabatic Electrode Stimulator With Inductive Energy Recycling and Feedback Current Regulation

Low-Power Circuits for Brain–Machine Interfaces

Fast startup CMOS current references

Wireless Neural Stimulation in Freely Behaving Small Animals

Low-Power Circuits for Brain-Machine Interfaces