High-Precision, Low-Threshold Neuromodulation With Ultraflexible Electrode Arrays for Brain-to-Brain Interfaces

Yifei Ye , Ye Tian , Haifeng Liu , Jiaxuan Liu , Cunkai Zhou , Chengjian Xu , Ting Zhou , Yanyan Nie , Yu Wu , Lunming Qin , Zhitao Zhou , Xiaoling Wei , Jianlong Zhao , Zhenyu Wang , Meng Li , Tiger H. Tao , Liuyang Sun

Exploration ›› 2025, Vol. 5 ›› Issue (4) : e70040

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Exploration ›› 2025, Vol. 5 ›› Issue (4) : e70040 DOI: 10.1002/EXP.70040
RESEARCH ARTICLE

High-Precision, Low-Threshold Neuromodulation With Ultraflexible Electrode Arrays for Brain-to-Brain Interfaces

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Abstract

Neuromodulation is crucial for advancing neuroscience and treating neurological disorders. However, traditional methods using rigid electrodes have been limited by large stimulating currents, low precision, and the risk of tissue damage. In this work, we developed a biocompatible ultraflexible electrode array that allows for both neural recording of spike firings and low-threshold, high-precision stimulation for neuromodulation. Specifically, mouse turning behavior can be effectively induced with approximately five microamperes of stimulating current, which is significantly lower than that required by conventional rigid electrodes. The array's densely packed microelectrodes enable highly selective stimulation, allowing precise targeting of specific brain areas critical for turning behavior. This low-current, targeted stimulation approach helps maintain the health of both neurons and electrodes, as evidenced by stable neural recordings after extended stimulations. Systematic validations have confirmed the durability and biocompatibility of the electrodes. Moreover, we extended the flexible electrode array to a brain-to-brain interface system that allows human brain signals to directly control mouse behavior. Using advanced decoding methods, a single individual can issue eight commands to simultaneously control the behaviors of two mice. This study underscores the effectiveness of the flexible electrode array in neuromodulation, opening new avenues for interspecies communication and potential neuromodulation applications.

Keywords

brain computer interface / brain-to-brain interface / flexible electrode array / neural interface / neuromodulation

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Yifei Ye, Ye Tian, Haifeng Liu, Jiaxuan Liu, Cunkai Zhou, Chengjian Xu, Ting Zhou, Yanyan Nie, Yu Wu, Lunming Qin, Zhitao Zhou, Xiaoling Wei, Jianlong Zhao, Zhenyu Wang, Meng Li, Tiger H. Tao, Liuyang Sun. High-Precision, Low-Threshold Neuromodulation With Ultraflexible Electrode Arrays for Brain-to-Brain Interfaces. Exploration, 2025, 5(4): e70040 DOI:10.1002/EXP.70040

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References

[1]

S. M. Won, E. Song, J. T. Reeder, and J. A. Rogers, “Emerging Modalities and Implantable Technologies for Neuromodulation,” Cell 181 (2020): 115-135.

[2]

a) E. Marder, “Neuromodulation of Neuronal Circuits: Back to the Future,” Neuron 76 (2012): 1-11. b) A. M. Lozano and N. Lipsman, “Probing and Regulating Dysfunctional Circuits Using Deep Brain Stimulation,” Neuron 77 (2013): 406-424. c) M. A. Lebedev and M. A. Nicolelis, “Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation,” Physiological Reviews 97 (2017): 767-837.

[3]

R. Chen, A. Canales, and P. Anikeeva, “Neural Recording and Modulation Technologies,” Nature Reviews Materials 2 (2017): 16093.

[4]

a) M. Hallett, “Transcranial Magnetic Stimulation: A Primer,” Neuron 55 (2007): 187-199. b) Z.-D. Deng, S. H. Lisanby, and A. V. Peterchev, “Electric Field Depth-Focality Tradeoff in Transcranial Magnetic Stimulation: Simulation Comparison of 50 Coil Designs,” Brain Stimulation 6 (2013): 1-13.

[5]

a) S. Jiang, X. Wu, N. J. Rommelfanger, Z. Ou, and G. Hong, “Shedding Light on Neurons: Optical Approaches for Neuromodulation,” National Science Review 9 (2022): nwac007. b) Y. Yang, M. Wu, A. Vazquez-Guardado, et al., “Wireless Multilateral Devices for Optogenetic Studies of Individual and Social Behaviors,” Nature Neuroscience 24 (2021): 1035-1045. c) L. Li, L. Lu, Y. Ren, et al., “Colocalized, Bidirectional Optogenetic Modulations in Freely Behaving Mice With a Wireless Dual-Color Optoelectronic Probe,” Nature Communications 13 (2022): 839.

[6]

X. S. Zheng, C. Tan, E. Castagnola, and X. T. Cui, “Electrode Materials for Chronic Electrical Microstimulation,” Advanced Healthcare Materials 10 (2021): e2100119.

[7]

M. M. Shanechi, “Brain-Machine Interfaces From Motor to Mood,” Nature Neuroscience 22 (2019): 1554-1564.

[8]

a) J. S. Perlmutter and J. W. Mink, “Deep Brain Stimulation,” Annual Review of Neuroscience 29 (2006): 229-257. b) L. V. Kalia and A. E. Lang, “Parkinson's Disease,” Lancet 386 (2015): 896-912.

[9]

J.-P. Nguyen, J. Nizard, Y. Keravel, and J.-P. Lefaucheur, “Invasive Brain Stimulation for the Treatment of Neuropathic Pain,” Nature Reviews Neurology 7 (2011): 699-709.

[10]

T. E. Schlaepfer, M. X. Cohen, C. Frick, et al., “Deep Brain Stimulation to Reward Circuitry Alleviates Anhedonia in Refractory Major Depression,” Neuropsychopharmacology 33 (2008): 368-377.

[11]

S. N. Flesher, J. E. Downey, J. M. Weiss, et al., “A Brain-Computer Interface that Evokes Tactile Sensations Improves Robotic Arm Control,” Science 372 (2021): 831-836.

[12]

a) X. Chen, F. Wang, E. Fernandez, and P. R. Roelfsema, “Shape Perception via a High-Channel-Count Neuroprosthesis in Monkey Visual Cortex,” Science 370 (2020): 1191-1196. b) S. F. Cogan, “Neural Stimulation and Recording Electrodes,” Annual Review of Biomedical Engineering 10 (2008): 275-309.

[13]

a) E. Otte, A. Vlachos, and M. Asplund, “Engineering Strategies Towards Overcoming Bleeding and Glial Scar Formation Around Neural Probes,” Cell and Tissue Research 387 (2022): 461-477. b) J. W. Salatino, K. A. Ludwig, T. D. Y. Kozai, and E. K. Purcell, “Glial Responses to Implanted Electrodes in the Brain,” Nature Biomedical Engineering 1 (2017): 862-877. c) P. Oldroyd and G. G. Malliaras, “Achieving Long-Term Stability of Thin-Film Electrodes for Neurostimulation,” Acta Biomaterialia 139 (2022): 65-81.

[14]

J. Wang, T. Wang, H. Liu, et al., “Flexible Electrodes for Brain-Computer Interface System,” Advanced Materials 35 (2023): e2211012.

[15]

a) L. Luan, X. Wei, Z. Zhao, et al., “Ultraflexible Nanoelectronic Probes Form Reliable, Glial Scar-Free Neural Integration,” Science Advances 3 (2017): e1601966. b) X. Wei, L. Luan, Z. Zhao, et al., “Nanofabricated Ultraflexible Electrode Arrays for High-Density Intracortical Recording,” Advanced Science 5 (2018): 1700625. c) E. Musk and Neuralink, “An Integrated Brain-Machine Interface Platform With Thousands of Channels,” Journal of Medical Internet Research 21 (2019): e16194.

[16]

R. Lycke, R. Kim, P. Zolotavin, et al., “Low-Threshold, High-Resolution, Chronically Stable Intracortical Microstimulation by Ultraflexible Electrodes,” Cell Reports 42 (2023): 112554.

[17]

a) B. Koo, C. S. Koh, H. Y. Park, et al., “Manipulation of Rat Movement via Nigrostriatal Stimulation Controlled by Human Visually Evoked Potentials,” Scientific Reports 7 (2017): 2340. b) M. Pais-Vieira, M. Lebedev, C. Kunicki, J. Wang, and M. A. Nicolelis, “A Brain-to-Brain Interface for Real-Time Sharing of Sensorimotor Information,” Scientific Reports 3 (2013): 1319.

[18]

a) V. Gradinaru, K. R. Thompson, F. Zhang, et al., “Targeting and Readout Strategies for Fast Optical Neural Control In Vitro and In Vivo,” Journal of Neuroscience 27 (2007): 14231-14238. b) X. Wu, Y. Jiang, N. J. Rommelfanger, et al., “Tether-Free Photothermal Deep-Brain Stimulation in Freely Behaving Mice via Wide-Field Illumination in the Near-Infrared-II Window,” Nature Biomedical Engineering 6 (2022): 754-770.

[19]

M. Tang, X. Zhang, A. Yang, et al., “Injectable Black Phosphorus Nanosheets for Wireless Nongenetic Neural Stimulation,” Small 18 (2022): e2105388.

[20]

F. Barthas and A. C. Kwan, “Secondary Motor Cortex: Where ‘Sensory’ Meets ‘Motor’ in the Rodent Frontal Cortex,” Trends in Neuroscience 40 (2017): 181-193.

[21]

R. Qazi, K. E. Parker, C. Y. Kim, et al., “Scalable and Modular Wireless-Network Infrastructure for Large-Scale Behavioural Neuroscience,” Nature Biomedical Engineering 6 (2022): 771-786.

[22]

G. Schiavone, X. Kang, F. Fallegger, J. Gandar, G. Courtine, and S. P. Lacour, “Guidelines to Study and Develop Soft Electrode Systems for Neural Stimulation,” Neuron 108 (2020): 238-258.

[23]

R. K. Shepherd, J. Villalobos, O. Burns, and D. A. X. Nayagam, “The Development of Neural Stimulators: A Review of Preclinical Safety and Efficacy Studies,” Journal of Neural Engineering 15 (2018): 041004.

[24]

a) Y. Tian, J. Yin, C. Wang, et al., “An Ultraflexible Electrode Array for Large-Scale Chronic Recording in the Nonhuman Primate Brain,” Advanced Science 10 (2023): e2302333. b) J. Fan, X. Li, P. Wang, et al., “A Hyperflexible Electrode Array for Long-Term Recording and Decoding of Intraspinal Neuronal Activity,” Advanced Science 10 (2023): e2303377.

[25]

a) C. Boehler, F. Oberueber, S. Schlabach, T. Stieglitz, and M. Asplund, “Long-Term Stable Adhesion for Conducting Polymers in Biomedical Applications: IrOx and Nanostructured Platinum Solve the Chronic Challenge,” ACS Applied Material Interfaces 9 (2017): 189-197. b) M. Ganji, L. Hossain, A. Tanaka, et al., “Monolithic and Scalable Au Nanorod Substrates Improve PEDOT-Metal Adhesion and Stability in Neural Electrodes,” Advanced Healthcare Materials 7 (2018): e1800923. c) A. Inoue, H. Yuk, B. Lu, and X. Zhao, “Strong Adhesion of Wet Conducting Polymers on Diverse Substrates,” Science Advances 6 (2020): eaay5394. d) S. Liu, Y. Wang, Y. Zhao, et al., “A Nanozyme-Based Electrode for High-Performance Neural Recording,” Advanced Materials 36 (2024): e2304297.

[26]

S. Venkatraman, J. Hendricks, Z. A. King, et al., “In Vitro and In Vivo Evaluation of PEDOT Microelectrodes for Neural Stimulation and Recording,” IEEE Transactions on Neural Systems and Rehabilitation Engineering 19 (2011): 307-316.

[27]

a) S. K. Talwar, S. Xu, E. S. Hawley, S. A. Weiss, K. A. Moxon, and J. K. Chapin, “Rat Navigation Guided by Remote Control,” Nature 417 (2002): 37-38. b) S. Khajei, V. Shalchyan, and M. R. Daliri, “Ratbot Navigation Using Deep Brain Stimulation in Ventral Posteromedial Nucleus,” Bioengineered 2019, 10, 250-260.

[28]

a) B. K. Min and K. R. Muller, “Electroencephalography/Sonication-Mediated Human Brain-Brain Interfacing Technology,” Trends in Biotechnology 32 (2014): 345-346. b) W. Lee, S. Kim, B. Kim, et al., “Non-Invasive Transmission of Sensorimotor Information in Humans Using an EEG/Focused Ultrasound Brain-to-Brain Interface,” PLoS ONE 12 (2017): e0178476. c) C. S. Nam, Z. Traylor, M. Chen, X. Jiang, W. Feng, and P. Y. Chhatbar, “Direct Communication Between Brains: A Systematic PRISMA Review of Brain-To-Brain Interface,” Frontiers in Neurorobotics 15 (2021): 656943. d) L. Lu, R. Wang, and M. Luo, “An Optical Brain-to-Brain Interface Supports Rapid Information Transmission for Precise Locomotion Control,” Science China Life Sciences 63 (2020): 875-885.

[29]

a) F. B. Vialatte, M. Maurice, J. Dauwels, and A. Cichocki, “Steady-State Visually Evoked Potentials: Focus on Essential Paradigms and Future Perspectives,” Progress in Neurobiology 90 (2010): 418-438. b) C. S. Herrmann, “Human EEG Responses to 1-100 Hz Flicker: Resonance Phenomena in Visual Cortex and Their Potential Correlation to Cognitive Phenomena,” Experimental Brain Research 137 (2001): 346-353. c) Z. Lin, C. Zhang, W. Wu, and X. Gao, “Frequency Recognition Based on Canonical Correlation Analysis for SSVEP-Based BCIs,” IEEE Transactions on Bio-Medical Engineering 54 (2007): 1172-1176.

[30]

a) M. Nakanishi, Y. J. Wang, X. G. Chen, Y. T. Wang, X. R. Gao, and T. P. Jung, “Enhancing Detection of SSVEPs for a High-Speed Brain Speller Using Task-Related Component Analysis,” IEEE Transactions on Biomedical Engineering 65 (2018): 104-112. b) X. Chen, Y. Wang, M. Nakanishi, X. Gao, T. P. Jung, and S. Gao, “High-Speed Spelling With a Noninvasive Brain-Computer Interface,” Proceedings National Academy of Science USA 112 (2015): E6058-E6067. c) Y. Zhang, S. N. Q. Xie, H. Wang, and Z. Q. Zhang, “Data Analytics in Steady-State Visual Evoked Potential-Based Brain-Computer Interface: A Review,” IEEE Sensors Journal 21 (2021): 1124-1138.

[31]

Y. Ye, Y. Tian, H. Wang, et al., “Flexible Bi-Directional Brain Computer Interface for Controlling Turning Behavior of Mice,” in Proceedings of the 2023 IEEE 36th International Conference on Micro Electro Mechanical Systems (MEMS) (IEEE, 2023), 33-36.

[32]

N. Yousif, R. Bayford, and X. Liu, “The Influence of Reactivity of the Electrode-Brain Interface on the Crossing Electric Current in Therapeutic Deep Brain Stimulation,” Neuroscience 156 (2008): 597-606.

[33]

K. A. Malaga, K. E. Schroeder, P. R. Patel, et al., “Data-Driven Model Comparing the Effects of Glial Scarring and Interface Interactions on Chronic Neural Recordings in Non-Human Primates,” Journal of Neural Engineering 13 (2016): 016010.

[34]

C. Sekirnjak, P. Hottowy, A. Sher, W. Dabrowski, A. M. Litke, and E. J. Chichilnisky, “Electrical Stimulation of Mammalian Retinal Ganglion Cells With Multielectrode Arrays,” Journal of Neurophysiology 95 (2006): 3311-3327.

[35]

D. Keller, C. Ero, and H. Markram, “Cell Densities in the Mouse Brain: A Systematic Review,” Frontiers in Neuroanatomy 12 (2018): 83.

[36]

Y. Wang, X. Chen, X. Gao, and S. Gao, “A Benchmark Dataset for SSVEP-Based Brain-Computer Interfaces,” IEEE Transactions on Neural Systems and Rehabilitation Engineering 25 (2016): 1746-1752.

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2025 The Author(s). Exploration published by Henan University and John Wiley & Sons Australia, Ltd.

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