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Register a new userStimDust: A mm-scale implantable wireless precision neural stimulator with ultrasonic power and communication
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Neural stimulation is a powerful technique for modulating physiological functions and for writing information into the nervous system as part of brain-machine interfaces. Current clinically approved neural stimulators require batteries and are many cubic centimetres in size -- typically much larger than their intended targets. We present a complete wireless neural stimulation system consisting of a 1.7 mm3 wireless, batteryless, leadless implantable stimulator (the mote), an ultrasonic wireless link for power and bi-directional communication, and a hand-held external transceiver. The mote consists of a piezoceramic transducer, an energy storage capacitor, and a stimulator integrated circuit (IC). The IC harvests ultrasonic power with high efficiency, decodes stimulation parameter downlink data, and generates current-controlled stimulation pulses. Stimulation parameters are time-encoded on the fly through the wireless link rather than being programmed and stored on the mote, reducing power consumption and on-chip memory requirements and enabling complex stimulation protocols with high-temporal resolution and low-latency feedback for use in closed-loop stimulation. Uplink data indicates whether the mote is currently stimulating; it is encoded by the mote via backscatter modulation and is demodulated at the external transceiver. We show that the mote operates at an acoustic intensity that is 7.8% of the FDA limit for diagnostic ultrasound and characterize the acoustic wireless links robustness to expected real-world misalignment. We demonstrate the in vivo performance of the system with motes acutely implanted with a cuff on the sciatic nerve of anesthetized rats and show highly repeatable stimulation across a wide range of physiological responses.
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The theory of communication through coherence (CTC) proposes that brain oscillations reflect changes in the excitability of neurons, and therefore the successful communication between two oscillating neural populations depends not only on the strength of the signal emitted but also on the relative phases between them. More precisely, effective communication occurs when the emitting and receiving populations are properly phase locked so the inputs sent by the emitting population arrive at the phases of maximal excitability of the receiving population. To study this setting, we consider a population rate model consisting of excitatory and inhibitory cells modelling the receiving population, and we perturb it with a time-dependent periodic function modelling the input from the emitting population. We consider the stroboscopic map for this system and compute numerically the fixed and periodic points of this map and their bifurcations as the amplitude and the frequency of the perturbation are varied. From the bifurcation diagram, we identify the phase-locked states as well as different regions of bistability. We explore carefully the dynamics emphasizing its implications for the CTC theory. In particular, we study how the input gain depends on the timing between the input and the inhibitory action of the receiving population. Our results show that naturally an optimal phase locking for CTC emerges, and provide a mechanism by which the receiving population can implement selective communication. Moreover, the presence of bistable regions, suggests a mechanism by which different communication regimes between brain areas can be established without changing the structure of the network
Wireless power transmission (WPT) is a critical technology that provides a secure alternative mechanism for wireless power and communication with implantable medical devices. WPT approaches for implantable medical devices have been utilized based on applications. For instance, the inductive coupling tactic is mostly employed for transmission of energy to neuro-stimulators, and the ultrasonic method is used for deep-seated implants. This article provides a study concentrating on popular WPT techniques for implantable medical devices (IMDs) including inductive coupling, microwave, ultrasound, and hybrid WPT systems consisting of two approaches combined. Moreover, an overview of the major works is analyzed with a comparison of their major design elements, operating frequency, distance, efficiency, and harvested power.
A 0.8 mm$^3$ wireless, ultrasonically powered, free-floating neural recording implant is presented. The device is comprised only of a 0.25 mm$^2$ recording IC and a single piezoceramic resonator that is used for both power harvesting and data transmission. Uplink data transmission is performed by analog amplitude modulation of the ultrasound echo. Using a 1.78 MHz main carrier, >35 kbps/mote equivalent uplink data rate is achieved. A technique to linearize the echo amplitude modulation is introduced, resulting in <1.2% static nonlinearity of the received signal over a $pm$10 mV input range. The IC dissipates 37.7 $mu$W, while the neural recording front-end consumes 4 $mu$W and achieves a noise floor of 5.3 $mu$V$_{rms}$ in a 5 kHz bandwidth. This work improves sub-mm recording mote depth by >2.5x, resulting in the highest measured depth/volume ratio by $sim$3x. Orthogonal subcarrier modulation enables simultaneous operation of multiple implants, using a single-element ultrasound external transducer. Dual-mote simultaneous power up and data transmission is demonstrated at a rate of 7 kS/s at the depth of 50 mm.
Intelligent reflecting surface (IRS) is a promising technology for wireless communications, thanks to its potential capability to engineer the radio environment. However, in practice, such an envisaged benefit is attainable only when the passive IRS is of a sufficiently large size, for which the conventional uniform plane wave (UPW)-based channel model may become inaccurate. In this paper, we pursue a new channel modelling and performance analysis for wireless communications with extremely large-scale IRS (XL-IRS). By taking into account the variations in signals amplitude and projected aperture across different reflecting elements, we derive both lower- and upper-bounds of the received signal-to-noise ratio (SNR) for the general uniform planar array (UPA)-based XL-IRS. Our results reveal that, instead of scaling quadratically with the increased number of reflecting elements M as in the conventional UPW model, the SNR under the more practically applicable non-UPW model increases with M only with a diminishing return and gets saturated eventually. To gain more insights, we further study the special case of uniform linear array (ULA)-based XL-IRS, for which a closed-form SNR expression in terms of the IRS size and transmitter/receiver location is derived. This result shows that the SNR mainly depends on the two geometric angles formed by the transmitter/receiver locations with the IRS, as well as the boundary points of the IRS. Numerical results validate our analysis and demonstrate the importance of proper channel modelling for wireless communications aided by XL-IRS.
This paper presents the first wireless and programmable neural stimulator leveraging magnetoelectric (ME) effects for power and data transfer. Thanks to low tissue absorption, low misalignment sensitivity and high power transfer efficiency, the ME effect enables safe delivery of high power levels (a few milliwatts) at low resonant frequencies (~250 kHz) to mm-sized implants deep inside the body (30-mm depth). The presented MagNI (Magnetoelectric Neural Implant) consists of a 1.5-mm$^2$ 180-nm CMOS chip, an in-house built 4x2 mm ME film, an energy storage capacitor, and on-board electrodes on a flexible polyimide substrate with a total volume of 8.2 mm$^3$ . The chip with a power consumption of 23.7 $mu$W includes robust system control and data recovery mechanisms under source amplitude variations (1-V variation tolerance). The system delivers fully-programmable bi-phasic current-controlled stimulation with patterns covering 0.05-to-1.5-mA amplitude, 64-to-512-$mu$s pulse width, and 0-to-200Hz repetition frequency for neurostimulation.
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