Collaborative Research: Large-Scale Wireless RF Networks of Microchip Sensors

The world around us is increasingly surrounded by electronic sensors. For applications such as wearable and implantable biomedical sensors there is a particular need and opportunity for unobtrusive microdevices which operate autonomously as large ensembles to map physiological activity across a body area of interest. A challenge is how to construct a wireless network whereby data from a microsensor population is transmitted, received, and decoded to unravel data, say from 1000 individual sensors.

A rough analogy is that of a population of common radio-frequency tags which must be read at once by a single transceiver - with the twist that signals at each sensor location will now vary both in time and in magnitude. A brain-computer interface suggests a paradigm in this context: how to capture neuronal signals at high resolution by a population of autonomous brain implanted microsensors.

Ongoing research for development of brain-machine interfaces in laboratories worldwide is focused on a number of schemes where access to thousands of points in the cortex is sought to translate brain computations to useful electronic commands e.g. for intended speech. The neurotechnology problem is three-fold: to record electrical signals from the brain unobtrusively, to transmit the data wirelessly to a body external receiver, and to decipher the multitude of signals in real time.

Many cases of distributed sensing of a dynamical environment are characterized by sparsity of events whether in nature or man-made systems, neurons in the brain being an example. The proposed event-driven communication strategy enables the efficient transmission, accurate retrieval, and interpretation of sparse events across a network of thousands of wireless microsensors – using the brain as an inspiration.

The proposed work is focused on an all-in-one approach to build a large scale wireless microsensor radio-frequency network. An external transceiver collects data while supplying wireless power to the sensors. Each sensor is a sub-millimeter size silicon system-on-microchip with custom circuitry designed for ‘event detection’ where time-varying sensor inputs are encoded as a series of short ‘spikes’.

The brain-inspired method of encoding data from sparse events has emerged recently in so- called dynamic vision cameras. Spike train data are converted into digital form on chip and transmitted to one common receiver. Since only the event-driven spikes are transmitted through the network, the bandwidth of the communication system can be utilized very efficiently enabling a large population of sensors to be incorporated into the network.

The team proposes to build a microsensor system and demonstrate low-error rate and efficient asynchronous, encoded wireless transmission in the laboratory using fabricated microchips, and to show extended applicability to thousands of nodes though simulations.

Importantly, the event-sensing detection and wireless communication approach is quite suited for a neuromorphic computational approach for analyzing multisensory data; the third key element in the project. The team will show how to decode actual brain data (synthesized elsewhere from actual primate brain recordings) from a hypothetical implant composed of up to 8000 microsensors.

Neuromorphic computing appears particularly suited decoding event-based data in terms of efficiency and short latency. Using available data from the primate motor cortex the team plans to show how to decode wireless signals from thousands of neurons for the prediction of planned arm and hand movement.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.