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Comparative analysis of energy transfer mechanisms for neural implants

Sols Miziev, Wiktoria Agata Pawlak, Newton Howard

2024Frontiers in Neuroscience20 citationsDOIOpen Access PDF

Abstract

As neural implant technologies advance rapidly, a nuanced understanding of their powering mechanisms becomes indispensable, especially given the long-term biocompatibility risks like oxidative stress and inflammation, which can be aggravated by recurrent surgeries, including battery replacements. This review delves into a comprehensive analysis, starting with biocompatibility considerations for both energy storage units and transfer methods. The review focuses on four main mechanisms for powering neural implants: Electromagnetic, Acoustic, Optical, and Direct Connection to the Body. Among these, Electromagnetic Methods include techniques such as Near-Field Communication (RF). Acoustic methods using high-frequency ultrasound offer advantages in power transmission efficiency and multi-node interrogation capabilities. Optical methods, although still in early development, show promising energy transmission efficiencies using Near-Infrared (NIR) light while avoiding electromagnetic interference. Direct connections, while efficient, pose substantial safety risks, including infection and micromotion disturbances within neural tissue. The review employs key metrics such as specific absorption rate (SAR) and energy transfer efficiency for a nuanced evaluation of these methods. It also discusses recent innovations like the Sectored-Multi Ring Ultrasonic Transducer (S-MRUT), Stentrode, and Neural Dust. Ultimately, this review aims to help researchers, clinicians, and engineers better understand the challenges of and potentially create new solutions for powering neural implants.

Topics & Concepts

Computer scienceEnergy harvestingTransducerTransmission (telecommunications)Maximum power transfer theoremEnergy transferEnergy (signal processing)TelecommunicationsPower (physics)Electrical engineeringEngineeringEngineering physicsPhysicsQuantum mechanicsWireless Power Transfer SystemsNeuroscience and Neural EngineeringEnergy Harvesting in Wireless Networks
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