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Lee J, Lee AH, Leung V, Laiwalla F, Lopez-Gordo MA, Larson L, Nurmikko A. An asynchronous wireless network for capturing event-driven data from large populations of autonomous sensors. NATURE ELECTRONICS 2024; 7:313-324. [PMID: 38737565 PMCID: PMC11078753 DOI: 10.1038/s41928-024-01134-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 02/15/2024] [Indexed: 05/14/2024]
Abstract
Networks of spatially distributed radiofrequency identification sensors could be used to collect data in wearable or implantable biomedical applications. However, the development of scalable networks remains challenging. Here we report a wireless radiofrequency network approach that can capture sparse event-driven data from large populations of spatially distributed autonomous microsensors. We use a spectrally efficient, low-error-rate asynchronous networking concept based on a code-division multiple-access method. We experimentally demonstrate the network performance of several dozen submillimetre-sized silicon microchips and complement this with large-scale in silico simulations. To test the notion that spike-based wireless communication can be matched with downstream sensor population analysis by neuromorphic computing techniques, we use a spiking neural network machine learning model to decode prerecorded open source data from eight thousand spiking neurons in the primate cortex for accurate prediction of hand movement in a cursor control task.
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Affiliation(s)
- Jihun Lee
- School of Engineering, Brown University, Providence, RI USA
| | - Ah-Hyoung Lee
- School of Engineering, Brown University, Providence, RI USA
| | - Vincent Leung
- Electrical and Computer Engineering, Baylor University, Waco, TX USA
| | - Farah Laiwalla
- School of Engineering, Brown University, Providence, RI USA
| | - Miguel Angel Lopez-Gordo
- Department of Signal Theory, Telematics and Communications, University of Granada, Granada, Spain
| | | | - Arto Nurmikko
- School of Engineering, Brown University, Providence, RI USA
- Carney Institute for Brain Science, Brown University, Providence, RI USA
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Nair V, Dalrymple AN, Yu Z, Balakrishnan G, Bettinger CJ, Weber DJ, Yang K, Robinson JT. Miniature battery-free bioelectronics. Science 2023; 382:eabn4732. [PMID: 37943926 DOI: 10.1126/science.abn4732] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 09/28/2023] [Indexed: 11/12/2023]
Abstract
Miniature wireless bioelectronic implants that can operate for extended periods of time can transform how we treat disorders by acting rapidly on precise nerves and organs in a way that drugs cannot. To reach this goal, materials and methods are needed to wirelessly transfer energy through the body or harvest energy from the body itself. We review some of the capabilities of emerging energy transfer methods to identify the performance envelope for existing technology and discover where opportunities lie to improve how much-and how efficiently-we can deliver energy to the tiny bioelectronic implants that can support emerging medical technologies.
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Affiliation(s)
- Vishnu Nair
- Rice Neuroengineering Initiative, Rice University, Houston, TX, USA
| | - Ashley N Dalrymple
- Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA
- Department of Physical Medicine and Rehabilitation, University of Utah, Salt Lake City, UT, USA
| | - Zhanghao Yu
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA
| | - Gaurav Balakrishnan
- Department of Materials Science & Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Christopher J Bettinger
- Department of Materials Science & Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Douglas J Weber
- Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA
- Center for Neural Basis of Cognition, Pittsburgh, PA, USA
| | - Kaiyuan Yang
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA
| | - Jacob T Robinson
- Rice Neuroengineering Initiative, Rice University, Houston, TX, USA
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA
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An H, Nason-Tomaszewski SR, Lim J, Kwon K, Willsey MS, Patil PG, Kim HS, Sylvester D, Chestek CA, Blaauw D. A Power-Efficient Brain-Machine Interface System With a Sub-mw Feature Extraction and Decoding ASIC Demonstrated in Nonhuman Primates. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2022; 16:395-408. [PMID: 35594208 PMCID: PMC9375520 DOI: 10.1109/tbcas.2022.3175926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Intracortical brain-machine interfaces have shown promise for restoring function to people with paralysis, but their translation to portable and implantable devices is hindered by their high power consumption. Recent devices have drastically reduced power consumption compared to standard experimental brain-machine interfaces, but still require wired or wireless connections to computing hardware for feature extraction and inference. Here, we introduce a Neural Recording And Decoding (NeuRAD) application specific integrated circuit (ASIC) in 180 nm CMOS that can extract neural spiking features and predict two-dimensional behaviors in real-time. To reduce amplifier and feature extraction power consumption, the NeuRAD has a hardware accelerator for extracting spiking band power (SBP) from intracortical spiking signals and includes an M0 processor with a fixed-point Matrix Acceleration Unit (MAU) for efficient and flexible decoding. We validated device functionality by recording SBP from a nonhuman primate implanted with a Utah microelectrode array and predicting the one- and two-dimensional finger movements the monkey was attempting to execute in closed-loop using a steady-state Kalman filter (SSKF). Using the NeuRAD's real-time predictions, the monkey achieved 100% success rate and 0.82 s mean target acquisition time to control one-dimensional finger movements using just 581 μW. To predict two-dimensional finger movements, the NeuRAD consumed 588 μW to enable the monkey to achieve a 96% success rate and 2.4 s mean acquisition time. By employing SBP, ASIC brain-machine interfaces can close the gap to enable fully implantable therapies for people with paralysis.
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