title: Model validation of untethered, ultrasonic neural dust motes for cortical recording.
abstract: A major hurdle in brain-machine interfaces (BMI) is the lack of an implantable neural interface system that remains viable for a substantial fraction of the user's lifetime. Recently, sub-mm implantable, wireless electromagnetic (EM) neural interfaces have been demonstrated in an effort to extend system longevity. However, EM systems do not scale down in size well due to the severe inefficiency of coupling radio-waves at those scales within tissue. This paper explores fundamental system design trade-offs as well as size, power, and bandwidth scaling limits of neural recording systems built from low-power electronics coupled with ultrasonic power delivery and backscatter communication. Such systems will require two fundamental technology innovations: (1) 10-100 μm scale, free-floating, independent sensor nodes, or neural dust, that detect and report local extracellular electrophysiological data via ultrasonic backscattering and (2) a sub-cranial ultrasonic interrogator that establishes power and communication links with the neural dust. We provide experimental verification that the predicted scaling effects follow theory; (127 μm)(3) neural dust motes immersed in water 3 cm from the interrogator couple with 0.002064% power transfer efficiency and 0.04246 ppm backscatter, resulting in a maximum received power of ∼0.5 μW with ∼1 nW of change in backscatter power with neural activity. The high efficiency of ultrasonic transmission can enable the scaling of the sensing nodes down to 10s of micrometer. We conclude with a brief discussion of the application of neural dust for both central and peripheral nervous system recordings, and perspectives on future research directions.
authors.affiliation: Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA. Electronic address: djseo@eecs.berkeley.edu.
authors.forename: Dongjin
authors.initials: D
authors.lastname: Seo
authors.affiliation: Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA; Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA.
authors.forename: Jose M
authors.initials: JM
authors.lastname: Carmena
authors.affiliation: Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA.
authors.forename: Jan M
authors.initials: JM
authors.lastname: Rabaey
authors.affiliation: Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA.
authors.forename: Michel M
authors.initials: MM
authors.lastname: Maharbiz
authors.affiliation: Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA.
authors.forename: Elad
authors.initials: E
authors.lastname: Alon
mesh_terms: D062207:Brain-Computer Interfaces; D002540:Cerebral Cortex / Q000502:physiology*; D006801:Humans; D008954:Models, Biological; D019736:Prostheses and Implants; D015203:Reproducibility of Results; D014465:Ultrasonics; D014584:User-Computer Interface; D059015:Wireless Technology
keywords: Backscatter communication; Brain–machine interfaces; Chronic extra-cellular recording systems; Ultrasonic energy harvesting