Magnetoelectric Nanoparticle-Based Wireless Brain–Computer Interface: Underlying Physics and Projected Technology Pathway

Abstract

Magnetoelectric nanoparticles (MENPs) provide a fully wireless and minutely invasive platform for bidirectional brain–computer interfaces (BCIs) by locally transducing magnetic fields into electric fields, and vice versa. The achievable spatial and temporal resolutions are governed by the control of magnetic field energy at the nanoparticle level. Since the introduction of the MENP concept a decade and a half ago, independent studies have demonstrated MENP-mediated neural activation in vitro and in vivo, establishing a strong proof of concept for wireless neuromodulation. In contrast, MENP-based neural recording remains largely theoretical, with existing models indicating that in vivo implementation is feasible. However, progress toward scalable and reliable MENP-based BCIs is hindered by an incomplete understanding of the nonlinear physics governing MENP operation and nanoparticle–cell interactions. This study addresses this gap by developing a comprehensive theoretical framework that explicitly incorporates nonlinear effects and correlates neuromodulation predictions with available experimental data. The analysis identifies nanoparticle properties and magnetic field amplitude and frequency as key performance determinants. Properly engineered MENPs are predicted to enable deepbrain and cortical neuromodulation and recording with submillimeter spatial resolution and millisecondscale temporal precision, offering a pathway toward clinically viable BCIs without implanted electrodes or genetic modification.