High-frequency, low-energy organic event-based sensors for closed-loop neurostimulation

Event-based bioelectronic sensors enable real-time detection and modulation of neural activity. However, conventional silicon interfaces are rigid and energy intensive, whereas organic electrochemical neuron (OECN)-based sensors, though promising, have been limited by slow firing rates, high energy use and scalability challenges. Here we present an OECN-based sensor capable of rapid, energy-efficient neural signal detection for closed-loop neurostimulation. These event-driven sensors respond within ~1 ms and generate voltage pulses up to 1.1 kHz, covering the full bandwidth of mammalian neuronal activity (0.5-1,000 Hz) while consuming only ~40 pJ per spike. Accurate detection of hippocampal interictal epileptiform discharges is demonstrated. Integrated with microelectrodes, these OECN-based sensors enable closed-loop neuromodulation by delivering real-time stimulation to suppress pathological sleep spindle oscillations in vivo. Combining biorealistic operation with ultra-low energy use, OECN-based sensors are good candidates for the next generation of implantable bioelectronics in energy-constrained environments.

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Publication Details

Journal
Nature Sensors
Published
2026-01-15
DOI
https://doi.org/10.1038/s44460-025-00007-x
Citations
2
Primary Topic
EEG and Brain-Computer Interfaces
Type
article
Field-Weighted Citation Impact
59.42

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article

High-frequency, low-energy organic event-based sensors for closed-loop neurostimulation

Dion Khodagholy, Jennifer N. Gelinas, Adam Marks, Junda Huang et al.
2 citations
Nature Sensors
EEG and Brain-Computer Interfaces
59.42
article

High-frequency, low-energy organic event-based sensors for closed-loop neurostimulation

Dion Khodagholy, Jennifer N. Gelinas, Adam Marks, Junda Huang, Junpeng Ji, Padinhare Cholakkal Harikesh, C.-L. Yang, Shao Shan, Deyu Tu, Han-Yan Wu, Simone Fabiano, Zifang Zhao, Xin-Yi Wang, Matteo Massetti, Magnus Berggren, Dace Gao, Miao Xiong, Tiefeng Liu, Iain McCulloch, Jian Pei
article en
2 citations

Abstract

Event-based bioelectronic sensors enable real-time detection and modulation of neural activity. However, conventional silicon interfaces are rigid and energy intensive, whereas organic electrochemical neuron (OECN)-based sensors, though promising, have been limited by slow firing rates, high energy use and scalability challenges. Here we present an OECN-based sensor capable of rapid, energy-efficient neural signal detection for closed-loop neurostimulation. These event-driven sensors respond within ~1 ms and generate voltage pulses up to 1.1 kHz, covering the full bandwidth of mammalian neuronal activity (0.5-1,000 Hz) while consuming only ~40 pJ per spike. Accurate detection of hippocampal interictal epileptiform discharges is demonstrated. Integrated with microelectrodes, these OECN-based sensors enable closed-loop neuromodulation by delivering real-time stimulation to suppress pathological sleep spindle oscillations in vivo. Combining biorealistic operation with ultra-low energy use, OECN-based sensors are good candidates for the next generation of implantable bioelectronics in energy-constrained environments.

Nature SensorsVol. 1(1)
Linköping University (SE), Princeton University (US), University of California, Irvine (US), Cornell University (US), University of Oxford (GB), Beijing National Laboratory for Molecular Sciences (CN), Wallenberg Wood Science Center (SE), Irvine University (US), Columbia University (US)
European Commission, Stiftelsen för Strategisk Forskning, National Natural Science Foundation of China, VINNOVA, Linköpings Universitet, Knut och Alice Wallenbergs Stiftelse, Vetenskapsrådet, Wallenberg Wood Science Center
Affordable and clean energy
Openalex Percentile: Top 0%
EEG and Brain-Computer Interfaces
59.42
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