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Name: Personal
Name: Personal
Name: Personal
Name: Personal
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Note: thesis: Thesis (Ph. D.)--Brown University, 2021
Genre (aat): theses
Abstract: This study focuses on the development of implantable biosensor microdevices for future brain-machine interfaces. Ensembles of spatially distributed sub-mm size devices, “neurograins, were designed to operate as a wireless sensor network. The core of each device is an ultralow-power silicon integrated circuit fabricated at the TSMC 65 nm MS/RF CMOS process. To enable wireless operation involving potentially large populations of miniaturized implants, an efficient microantenna transceiver scheme was developed, allowing for simultaneous powering and bidirectional data communication at near 1 GHz to/from an external RF hub. Each neurograin (650 μm × 650 μm in the area), one node houses an on-chip coil, an RF energy harvesting circuit, custom circuits for either electrophysiological recording or electrical microstimulation, device identifier, plus specialized circuits for telemetry. Implementing a binary phase-shift keying modulation (BPSK) scheme on-chip was demonstrated to ensure a 10 Mbps data uplink using RF backscattering from each neurograin. An amplitude shift keying and pulse width modulation demodulation (ASK-PWM) scheme was advanced to achieve sufficient network robustness under asynchronous clock conditions across the neurograin population at 1 Mbps downlink rate. Based on simulations and experiments, we have demonstrated how a bidirectional uplink/ downlink can communicate with the external telecom hub for up to 770 neurograins under a customized time division multiple access (TDMA) protocol operating in a call-and-response manner. Extensive characterization of the neurograin ecosystem including wireless networking, wireless powering, and electrophysiological recording or stimulation was performed both on benchtop and in-vivo. Finally, we have demonstrated a multinodal network by implanting populations of neurograins into the rat model for wireless recording and microstimulation of cortical dynamics, with the animal head size limiting the scale of the implant to 48 neurograins. This thesis also describes neural population recording and decoding from the auditory cortex of non-human primates, which employed a platform, called “Dockex” previously developed in our group, for scalable machine learning experiments. The success in these scalable data acquisition and decoding approaches is encouraging further development of the concept of large-scale cortical interfaces in primates.
Subject (fast) (authorityURI="http://id.worldcat.org/fast", valueURI="http://id.worldcat.org/fast/01764283")
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Type of Resource (primo): dissertations