Overview

Electronic sensors are increasingly prevalent in the world around us. Scalability, however, is complicated by the fact that the signals at each sensor location can vary both in time and in magnitude, but often must be read at once by a single transceiver. Current brain-computer interface research, as an example, is hampered by inefficiencies and bandwidth constraints. Our solution uses the brain as inspiration, and provides an event-driven communication strategy that enables the efficient transmission, accurate retrieval and interpretation of sparse events across a network of thousands of wireless microsensors.

Market Opportunity

For applications such as wearable and implantable biomedical sensors, there is a particular need for unobtrusive microdevices that 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 aggregate data from a large microsensor population is transmitted, received and decoded, to unpack data from the individual sensors.

Innovation and Meaningful Advantages

Our technology draws on current understanding of the brain’s information processing, whereby neuronal packets of information are sparse, binary "spike-firing" events. Our large-scale wireless biosensor networks measure internal states of the body via unobtrusive, spatially distributed silicon chiplets that are either implanted in the body or applied to the surface of the skin. Physiological signals measured locally from each autonomous microsensor are transmitted wirelessly in real time, using radio frequency transmission. An external transceiver collects data while supplying wireless power to the sensors.

Each sensor is designed for event detection, which involves the conversion of time-varying sensor inputs into a series of short ‘spikes’. The spike train data are then converted into digital form on chip and transmitted to a common receiver. As only the event-driven spikes are transmitted through the network, the bandwidth of the communication system can be used very efficiently, enabling scaling to thousands of sensors in the network.  Beyond contributing to brain–computer interface technology, this approach could potentially be applied to a broad range of technologies that track and characterize complex and dynamic environments in real time.

Collaboration Opportunity

We are interested in exploring 1) startup opportunities with investors in the medical device space; 2) research collaborations with leading medical device companies to develop this technology; and 3) licensing opportunities with medical device companies.

Principal Investigator

Arto V. Nurmikko, PhD

L. Herbert Ballou University Professor of Engineering

Professor of Physics

Brown University

arto_nurmikko@brown.edu

https://vivo.brown.edu/display/anurmikk

Publications

Lee, J., Lee, AH., Leung, V. et al. An asynchronous wireless network for capturing event-driven data from large populations of autonomous sensors. Nat Electron 7, 313–324 (2024). https://doi.org/10.1038/s41928-024-01134-y

Wireless radiofrequency network of distributed microsensors. Nat Electron 7, 264–265 (2024). https://doi.org/10.1038/s41928-024-01141-z

IP Information

Allowed US Utility Application 18/048,313 Filed October 20, 2022

Contact

Melissa Simon, PhD

Director of Business Development

melissa_j_simon@brown.edu

Brown Tech ID 3186