An Adaptable Lateral Resolution Acoustic Beamforming for Network of Wireless Neural Interfaces
Wireless Neural Interfaces are minimally invasive untethered links between the brain tissue and silicon platforms. Even though these interfaces have been envisioned for many biomedical applications, it is unclear how the ultimate technology will be, due to the lack of studies that support spatially distributed networks. In this paper, we contribute to the first step towards a networked wireless neural interfaces scenario by addressing the distributed power allocation through adaptable beamforming by varying the acoustic beam lateral resolution. Our technique provides selectivity and depthness of neural interface nodes inside the Brain’s neocortex. Our results show improvements in average power transfer efficiency for sparser beams compared to narrower ones for a randomly placed network of implantable devices with 15 nodes within a 4mm2 space in the neocortex. Lateral resolution indeed achieves selective of the devices, the average efficiency was non-constant throughout the network allowing devices to operate in different time scales suitable for asynchronous networks. Enabling a functioning network is essential for providing an impactful interface with the Brain to allow future usage of this technology in a diverse set of neurological diseases theranostics.