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This paper presents a concept for a double negative metamaterial (DNM)-based antenna to simultaneously enhance Wireless Power Transfer (WPT) and reduce Specific Absorption Rate (SAR) here for a network of distributed brain microimplants. The DNM copper coils are integrated in a FR-4 substrate, which has a dielectric constant of 4.3 and tangent loss (δ) of 0.025. Occupying a 2 × 2 cm2 area, the DNM structure is introduced into our target wireless brain-machine interface (BMI) system operating at 915 MHz. Preliminary HFSS simulations show it provides 2 dB WPT enhancement and a 20% SAR reduction. We believe the work has the potential to address the WPT/ SAR co-optimization challenges for biomedical implants in general. 

Wireless sub-mm sized distributed brain implants have been proposed as the next frontier of Brain-Machine Interface (BMI) design to achieve untethered, high-density neural recording and stimulation. Simultaneously improving the wireless power transfer (WPT) efficiency and reducing the specific absorption rate (SAR) will be crucial for its clinical success. Towards these goals, we present an EM simulation method, a lumped equivalent circuit model, and a theoretical analysis to accurately predict the power delivered to the recording/ stimulating nodes, as well as the power dissipated in biological tissues and all other lossy elements within the system. This comprehensive framework also explains how increasing the distance between the transmit coil and the scalp can beneficially reduce the SAR without undermining the WPT efficiency. This work presents a rigorous prediction technique for transmission loss and tissue heating towards performance optimization. 

Abstract Networks of spatially distributed radiofrequency identification sensors could be used to collect data in wearable or implantable biomedical applications. However, the development of scalable networks remains challenging. Here we report a wireless radiofrequency network approach that can capture sparse event-driven data from large populations of spatially distributed autonomous microsensors. We use a spectrally efficient, low-error-rate asynchronous networking concept based on a code-division multiple-access method. We experimentally demonstrate the network performance of several dozen submillimetre-sized silicon microchips and complement this with large-scale in silico simulations. To test the notion that spike-based wireless communication can be matched with downstream sensor population analysis by neuromorphic computing techniques, we use a spiking neural network machine learning model to decode prerecorded open source data from eight thousand spiking neurons in the primate cortex for accurate prediction of hand movement in a cursor control task.