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Neuroscience-Inspired Plant Electrophysiology: From Signal Decoding to Plant-Computer Interfaces
Review Article  ·  Published: 30 June 2026
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Journal of Plant Electrobiology
Volume 1, Issue 2, 2026: 123-140
Review Article Free to Read

Neuroscience-Inspired Plant Electrophysiology: From Signal Decoding to Plant-Computer Interfaces

by
Ziyang Wang Ziyang Wang 1 * ORCID
Fangmei Yang Fangmei Yang 1 ORCID
Ruihang Zhang Ruihang Zhang 2
Dongjie Zhao Dongjie Zhao 3 ORCID
1 Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
2 School of Information and Communication Engineering, Beijing Information Science and Technology University, Beijing 100101, China
3 School of Automation, Qingdao University, Qingdao 266071, China
* Corresponding Author: Ziyang Wang, [email protected]
Volume 1, Issue 2
Volume 1, Issue 2 2026
Received: 03 March 2026 Accepted: 24 June 2026 Published: 30 June 2026 CrossMark
Download PDF 1.82 MB Cite Metrics
DOI: 10.62762/JPE.2026.744863
ARK: ark:/57805/jpe.2026.744863

Article Information

Published in Journal of Plant Electrobiology
Volume/Issue Volume 1, Issue 2, 2026
Pages 123-140

Abstract

Plant electrophysiology is undergoing a profound paradigm shift from traditional phenomenological observation to systemic signal decoding, with mature methodologies from computational neuroscience and brain-computer interface technologies providing critical theoretical and engineering support for this interdisciplinary evolution. This review first systematically summarizes the evolution of flexible wearable electrodes and ultra-high impedance amplification hardware systems tailored to the ultra-slow signal dynamics and continuous morphological growth characteristics of plants. Second, we discuss the application pathways of introducing standardized sequential evoked paradigms from neuroscience—such as steady-state visual evoked potentials and event-related potentials—into the plant domain. This aims to replace traditional destructive stimuli with non-invasive, reproducible rhythmic stimulation to acquire data with high signal-to-noise ratios. In the dimension of data analysis, we explore modeling strategies that incorporate physics-informed neural networks and multi-modal heterogeneous sensor fusion technologies under the constraint of sample scarcity, aiming to resolve the equifinality problem inherent in single-modality electrical signal decoding. Building upon this decoding foundation, this paper proposes the construction of a bidirectional Plant-Computer Interface architecture, exploring the engineering feasibility of utilizing the plant itself as an active sensory node to directly drive closed-loop regulation within agricultural environments. Establishing cross-species standardized open-source datasets and unified hardware/software testing benchmarks will be the core driving force in overcoming current data fragmentation. Ultimately, the deep integration of multidisciplinary approaches will lay a rigorous scientific foundation for precision agricultural resource management and the development of next-generation bio-inspired intelligent hardware.

Graphical Abstract

Neuroscience-Inspired Plant Electrophysiology: From Signal Decoding to Plant-Computer Interfaces

Keywords

plant electrophysiology plant-computer interface multi-modal fusion signal decoding plant-in-the-loop soft robotics

Data Availability Statement

Not applicable.

Funding

This work was supported by the Development and Promotion of a Flexible, Low-Damage Qingdao Key Technology R&D Project: Multi-Row Intelligent Fresh Corn Combine Harvester.

Conflicts of Interest

The authors declare no conflicts of interest.

AI Use Statement

The authors declare that Gemini 3.1 was used for language editing and translation of the manuscript. The authors have carefully reviewed, revised, and verified the AI-assisted output and take full responsibility for the content of the manuscript.

Ethical Approval and Consent to Participate

Not applicable.

References

  1. Sanderson, J. B. (1872). Note on the Electrical Phenomena Which Accompany Irritation of the Leaf of Dionaea muscipula. Proceedings of the Royal Society of London, 495-496.
    [CrossRef] [Google Scholar]
  2. Brenner, E. D., Stahlberg, R., Mancuso, S., Vivanco, J., Baluška, F., & Van Volkenburgh, E. (2006). Plant neurobiology: an integrated view of plant signaling. Trends in plant science, 11(8), 413-419.
    [CrossRef] [Google Scholar]
  3. Stahlberg, R. (2006). Historical overview on plant neurobiology. Plant Signaling & Behavior, 1(1), 6-8.
    [CrossRef] [Google Scholar]
  4. Shepherd, V. A. (2012). At the roots of plant neurobiology. In Plant electrophysiology: Methods and cell electrophysiology (pp. 3-43). Berlin, Heidelberg: Springer Berlin Heidelberg.
    [CrossRef] [Google Scholar]
  5. Davies, E. (2006). Electrical signals in plants: facts and hypotheses. In Plant electrophysiology: theory and methods (pp. 407-422). Berlin, Heidelberg: Springer Berlin Heidelberg.
    [CrossRef] [Google Scholar]
  6. Yan, X., Wang, Z., Huang, L., Wang, C., Hou, R., Xu, Z., & Qiao, X. (2009). Research progress on electrical signals in higher plants. Progress in Natural Science, 19(5), 531-541.
    [CrossRef] [Google Scholar]
  7. Li, J. H., Fan, L. F., Zhao, D. J., Zhou, Q., Yao, J. P., Wang, Z. Y., & Huang, L. (2021). Plant electrical signals: A multidisciplinary challenge. Journal of Plant Physiology, 261, 153418.
    [CrossRef] [Google Scholar]
  8. Fromm, J., & Lautner, S. (2007). Electrical signals and their physiological significance in plants. Plant, cell & environment, 30(3), 249-257.
    [CrossRef] [Google Scholar]
  9. Baluška, F., & Mancuso, S. (2009). Plant neurobiology: from sensory biology, via plant communication, to social plant behavior. Cognitive processing, 10(Suppl 1), 3-7.
    [CrossRef] [Google Scholar]
  10. Baluška, F., Mancuso, S., Volkmann, D., & Barlow, P. (2009). The ‘root-brain’hypothesis of Charles and Francis Darwin: revival after more than 125 years. Plant signaling & behavior, 4(12), 1121-1127.
    [CrossRef] [Google Scholar]
  11. Kawano, T., Ushifusa, Y., Mancuso, S., Baluška, F., Sylvain-Bonfanti, L., Arbelet-Bonnin, D., & Bouteau, F. (2025). Plants have two minds as we do. Plant Signaling & Behavior, 20(1), 2474895.
    [CrossRef] [Google Scholar]
  12. Minorsky, P. V. (2024). The “plant neurobiology” revolution. Plant Signaling & Behavior, 19(1), 2345413.
    [CrossRef] [Google Scholar]
  13. Drew, B. J., Califf, R. M., Funk, M., Kaufman, E. S., Krucoff, M. W., Laks, M. M., ... & Van Hare, G. F. (2004). Practice standards for electrocardiographic monitoring in hospital settings: an American Heart Association scientific statement from the Councils on Cardiovascular Nursing, Clinical Cardiology, and Cardiovascular Disease in the Young: endorsed by the International Society of Computerized Electrocardiology and the American Association of Critical-Care Nurses. Circulation, 110(17), 2721-2746.
    [CrossRef] [Google Scholar]
  14. Benbadis, S. R., Beniczky, S., Bertram, E., MacIver, S., & Moshé, S. L. (2020). The role of EEG in patients with suspected epilepsy. Epileptic Disorders, 22(2), 143-155.
    [CrossRef] [Google Scholar]
  15. Koravand, A., Jutras, B., & Lassonde, M. (2013). Auditory event related potentials in children with peripheral hearing loss. Clinical Neurophysiology, 124(7), 1439-1447.
    [CrossRef] [Google Scholar]
  16. Lavazza, A., Balconi, M., Ienca, M., Minerva, F., Pizzetti, F. G., Reichlin, M., ... & Songhorian, S. (2025). Neuralink’s brain-computer interfaces: medical innovations and ethical challenges. Frontiers in Human Dynamics, 7, 1553905.
    [CrossRef] [Google Scholar]
  17. Wang, Z. Y., Qin, X. H., Li, J. H., Fan, L. F., Zhou, Q., Wang, Y. Q., ... & Huang, L. (2019). Highly reproducible periodic electrical potential changes associated with salt tolerance in wheat plants. Environmental and Experimental Botany, 160, 120-130.
    [CrossRef] [Google Scholar]
  18. Wang, Z., Fan, L., Wang, Y., Li, J., Zhou, Q., Huang, L., & Wang, Z. (2018). Selection of recording pattern of plant surface electrical signal based on analysis of electrical characteristics. Transactions of the Chinese Society of Agricultural Engineering, 34(5), 137-143. https://www.ingentaconnect.com/content/tcsae/tcsae/2018/00000034/00000005/art00018
    [Google Scholar]
  19. Zhai, J., Li, A., Dong, H., Jin, X., Luo, B., & Wang, X. (2025). Ion-selective electrodes: innovations for precision in vivo plant ion monitoring. Microchimica Acta, 192(11), 1-26.
    [CrossRef] [Google Scholar]
  20. Zhou, J., Fan, P., Zhou, S., Pan, Y., & Ping, J. (2025). Machine learning-assisted implantable plant electrophysiology microneedle sensor for plant stress monitoring. Biosensors and Bioelectronics, 271, 117062.
    [CrossRef] [Google Scholar]
  21. Yang, Y., He, T., Ravindran, P., Wen, F., Krishnamurthy, P., Wang, L., ... & Lee, C. (2024). All-organic transparent plant e-skin for noninvasive phenotyping. Science Advances, 10(7), eadk7488.
    [CrossRef] [Google Scholar]
  22. Taniguchi, H., Akiyama, K., & Fujie, T. (2020). Biopotential measurement of plant leaves with ultra-light and flexible conductive polymer nanosheets. Bulletin of the Chemical Society of Japan, 93(8), 1007-1013.
    [CrossRef] [Google Scholar]
  23. Zhou, S., Zhou, J., Pan, Y., Wu, Q., & Ping, J. (2024). Wearable electrochemical sensors for plant small-molecule detection. Trends in Plant Science, 29(2), 219-231.
    [CrossRef] [Google Scholar]
  24. Kroeger, J. H., Zerzour, R., & Geitmann, A. (2011). Regulator or driving force? The role of turgor pressure in oscillatory plant cell growth. PloS one, 6(4), e18549.
    [CrossRef] [Google Scholar]
  25. Wang, Z. Y., Li, J. H., Zhou, Q., Gao, X. Y., Fan, L. F., Wang, Y. Q., ... & Huang, L. (2018). Multi-channel system for simultaneous in situ monitoring of ion flux and membrane potential in plant electrophysiology. IEEE Access, 7, 4688-4697.
    [CrossRef] [Google Scholar]
  26. Nelson, M. J., Pouget, P., Nilsen, E. A., Patten, C. D., & Schall, J. D. (2008). Review of signal distortion through metal microelectrode recording circuits and filters. Journal of neuroscience methods, 169(1), 141-157.
    [CrossRef] [Google Scholar]
  27. Bera, T. K., Bera, S., Kar, K., & Mondal, S. (2016). Studying the variations of complex electrical bio-impedance of plant tissues during boiling. Procedia Technology, 23, 248-255.
    [CrossRef] [Google Scholar]
  28. Tsuchida, T. N., Acharya, J. N., Halford, J. J., Kuratani, J. D., Sinha, S. R., Stecker, M. M., ... & Drislane, F. W. (2016). American clinical neurophysiology society: EEG guidelines introduction. Journal of Clinical Neurophysiology, 33(4), 301-302.
    [CrossRef] [Google Scholar]
  29. Masi, E., Ciszak, M., Stefano, G., Renna, L., Azzarello, E., Pandolfi, C., ... & Mancuso, S. (2009). Spatiotemporal dynamics of the electrical network activity in the root apex. Proceedings of the National Academy of Sciences, 106(10), 4048-4053.
    [CrossRef] [Google Scholar]
  30. Zhang, Y., Yuan, Y., Duan, H., Zhu, P., & Mao, Y. (2025). Bionic hydrogel-based stretchable devices for bioelectronics applications. Journal of Bionic Engineering, 22(3), 982-1013.
    [CrossRef] [Google Scholar]
  31. Wang, H., Zhang, Y., He, X., Zuo, F., Yang, Y., Yan, P., ... & He, S. (2023). Wettability of liquid metals on PEDOT: PSS for soft electronics. Applied Surface Science, 609, 155410.
    [CrossRef] [Google Scholar]
  32. Lee, G., Hossain, O., Jamalzadegan, S., Liu, Y., Wang, H., Saville, A. C., ... & Wei, Q. (2023). Abaxial leaf surface-mounted multimodal wearable sensor for continuous plant physiology monitoring. Science Advances, 9(15), eade2232.
    [CrossRef] [Google Scholar]
  33. Felle, H. H., & Zimmermann, M. R. (2007). Systemic signalling in barley through action potentials. Planta, 226(1), 203-214.
    [CrossRef] [Google Scholar]
  34. Ognard, J., El Hajj, G., Verma, O., Ghozy, S., Kadirvel, R., Kallmes, D. F., & Brinjikji, W. (2025). Advances in endovascular brain computer interface: Systematic review and future implications. Journal of Neuroscience Methods, 420, 110471.
    [CrossRef] [Google Scholar]
  35. Niedermeyer, E., & da Silva, F. L. (Eds.). (2005). Electroencephalography: basic principles, clinical applications, and related fields. Lippincott Williams & Wilkins.
    [Google Scholar]
  36. Steinmetz, N. A., Koch, C., Harris, K. D., & Carandini, M. (2018). Challenges and opportunities for large-scale electrophysiology with Neuropixels probes. Current opinion in neurobiology, 50, 92-100.
    [CrossRef] [Google Scholar]
  37. Mudrilov, M., Ladeynova, M., Grinberg, M., Balalaeva, I., & Vodeneev, V. (2021). Electrical signaling of plants under abiotic stressors: transmission of stimulus-specific information. International Journal of Molecular Sciences, 22(19), 10715.
    [CrossRef] [Google Scholar]
  38. Yao, J. P., Wang, Z. Y., de Oliveira, R. F., Wang, Z. Y., & Huang, L. (2021). A deep learning method for the long-term prediction of plant electrical signals under salt stress to identify salt tolerance. Computers and Electronics in Agriculture, 190, 106435.
    [CrossRef] [Google Scholar]
  39. Stett, A., Egert, U., Guenther, E., Hofmann, F., Meyer, T., Nisch, W., & Haemmerle, H. (2003). Biological application of microelectrode arrays in drug discovery and basic research. Analytical and bioanalytical chemistry, 377(3), 486-495.
    [CrossRef] [Google Scholar]
  40. Rubehn, B., Bosman, C., Oostenveld, R., Fries, P., & Stieglitz, T. (2009). A MEMS-based flexible multichannel ECoG-electrode array. Journal of neural engineering, 6(3), 036003.
    [CrossRef] [Google Scholar]
  41. Mohammed, A. H., Cabrerizo, M., Pinzon, A., Yaylali, I., Jayakar, P., & Adjouadi, M. (2023). Graph neural networks in EEG spike detection. Artificial Intelligence in Medicine, 145, 102663.
    [CrossRef] [Google Scholar]
  42. Iannetti, G. D., Hughes, N. P., Lee, M. C., & Mouraux, A. (2008). Determinants of laser-evoked EEG responses: pain perception or stimulus saliency?. Journal of neurophysiology, 100(2), 815-828.
    [CrossRef] [Google Scholar]
  43. Sukhova, E., Yudina, L., Kozlova, E., & Sukhov, V. (2024). Preliminary Treatment by Exogenous 24-Epibrassinolide Influences Burning-Induced Electrical Signals and Following Photosynthetic Responses in Pea (Pisum sativum L.). Plants, 13(23), 3292.
    [CrossRef] [Google Scholar]
  44. Yudina, L., Popova, A., Zolin, Y., Sukhova, E., & Sukhov, V. (2023). Local action of increased pressure induces hyperpolarization electrical signals and influences photosynthetic light reactions in wheat plants. Plants, 12(13), 2570.
    [CrossRef] [Google Scholar]
  45. Shabala, S., & Newman, I. (1999). Light-induced changes in hydrogen, calcium, potassium, and chloride ion fluxes and concentrations from the mesophyll and epidermal tissues of bean leaves. Understanding the ionic basis of light-induced bioelectrogenesis. Plant Physiology, 119(3), 1115-1124.
    [CrossRef] [Google Scholar]
  46. Shabala, S., Demidchik, V., Shabala, L., Cuin, T. A., Smith, S. J., Miller, A. J., ... & Newman, I. A. (2006). Extracellular Ca2+ ameliorates NaCl-induced K+ loss from Arabidopsis root and leaf cells by controlling plasma membrane K+-permeable channels. Plant physiology, 141(4), 1653-1665.
    [CrossRef] [Google Scholar]
  47. Li, J., Yue, Y., Wang, Z., Zhou, Q., Fan, L., Chai, Z., ... & Huang, L. (2019). Illumination/darkness-induced changes in leaf surface potential linked with kinetics of ion fluxes. Frontiers in plant science, 10, 1407.
    [CrossRef] [Google Scholar]
  48. Lim, C. L., Gordon, E., Rennie, C., Wright, J. J., Bahramali, H., Li, W. M., ... & Morris, J. G. L. (1999). Dynamics of SCR, EEG, and ERP activity in an oddball paradigm with short interstimulus intervals. Psychophysiology, 36(5), 543-551.
    [CrossRef] [Google Scholar]
  49. Wang, Y., Chen, X., Gao, X., & Gao, S. (2016). A benchmark dataset for SSVEP-based brain–computer interfaces. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25(10), 1746-1752.
    [CrossRef] [Google Scholar]
  50. Li, Y., Bin, G., Gao, X., Hong, B., & Gao, S. (2011, April). Analysis of phase coding SSVEP based on canonical correlation analysis (CCA). In 2011 5th international IEEE/eMBS conference on neural engineering (pp. 368-371). IEEE.
    [CrossRef] [Google Scholar]
  51. Amiri, S., Rabbi, A., Azinfar, L., & Fazel-Rezai, R. (2013). A review of P300, SSVEP, and hybrid P300/SSVEP brain-computer interface systems. In R. Fazel-Rezai (Ed.), Brain-Computer Interface Systems - Recent Progress and Future Prospects. IntechOpen.
    [CrossRef] [Google Scholar]
  52. Lamti, H. A., Khelifa, M. M. B., Alimi, A. M., & Gorce, P. (2014, August). Influence of mental fatigue on P300 and SSVEP during virtual wheelchair navigation. In 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 1255-1258). IEEE.
    [CrossRef] [Google Scholar]
  53. Kwak, N. S., Müller, K. R., & Lee, S. W. (2015). A lower limb exoskeleton control system based on steady state visual evoked potentials. Journal of neural engineering, 12(5), 056009.
    [CrossRef] [Google Scholar]
  54. Kelly, S. P., Lalor, E. C., Reilly, R. B., & Foxe, J. J. (2005). Visual spatial attention tracking using high-density SSVEP data for independent brain-computer communication. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 13(2), 172-178.
    [CrossRef] [Google Scholar]
  55. Chapman, R. M., & Bragdon, H. R. (1964). Evoked responses to numerical and non-numerical visual stimuli while problem solving. Nature, 203(4950), 1155-1157.
    [CrossRef] [Google Scholar]
  56. Picton, T. W., Lins, O. G., & Scherg, M. (1995). The recording and analysis of event-related potentials. Handbook of neuropsychology, 10, 3-73. https://www.researchgate.net/publication/247966238
    [Google Scholar]
  57. Kutas, M., & Federmeier, K. D. (2011). Thirty years and counting: finding meaning in the N400 component of the event-related brain potential (ERP). Annual review of psychology, 62(1), 621-647.
    [CrossRef] [Google Scholar]
  58. Krusienski, D. J., Sellers, E. W., McFarland, D. J., Vaughan, T. M., & Wolpaw, J. R. (2008). Toward enhanced P300 speller performance. Journal of neuroscience methods, 167(1), 15-21.
    [CrossRef] [Google Scholar]
  59. de Munck, J. C., Vijn, P. C., & da Silva, F. L. (1992). A random dipole model for spontaneous brain activity. IEEE transactions on biomedical engineering, 39(8), 791-804.
    [CrossRef] [Google Scholar]
  60. Li, J., & Zhang, L. (2012). Active training paradigm for motor imagery BCI. Experimental brain research, 219(2), 245-254.
    [CrossRef] [Google Scholar]
  61. Shabala, S. N., Newman, I. A., & Morris, J. (1997). Oscillations in H+ and Ca2+ ion fluxes around the elongation region of corn roots and effects of external pH. Plant Physiology, 113(1), 111-118.
    [CrossRef] [Google Scholar]
  62. Gallé, A., Lautner, S., Flexas, J., & Fromm, J. (2015). Environmental stimuli and physiological responses: the current view on electrical signalling. Environmental and Experimental Botany, 114, 15-21.
    [CrossRef] [Google Scholar]
  63. Hedrich, R., & Kreuzer, I. (2026). The power of ionic movements in plants. New Phytologist, 249(5), 2232-2240.
    [CrossRef] [Google Scholar]
  64. Zimmermann, M. R., & Mithöfer, A. (2013). Electrical long-distance signaling in plants. In Long-distance systemic signaling and communication in plants (pp. 291-308). Berlin, Heidelberg: Springer Berlin Heidelberg.
    [CrossRef] [Google Scholar]
  65. de Carvalho Oliveira, T. F., Costa, Á. V. L., Posso, D. A., Reissig, G. N., & Souza, G. M. (2025). Time dispersion analysis of features as a tool for investigating plant electrophysiology: A case study using moderate magnetic field in bean plants: TF de Carvalho Oliveira et al. Journal of Biological Physics, 51(1), 28.
    [CrossRef] [Google Scholar]
  66. Zimmermann, M. R., Maischak, H., Mithofer, A., Boland, W., & Felle, H. H. (2009). System potentials, a novel electrical long-distance apoplastic signal in plants, induced by wounding. Plant physiology, 149(3), 1593-1600.
    [CrossRef] [Google Scholar]
  67. Qin, X. H., Wang, Z. Y., Yao, J. P., Zhou, Q., Zhao, P. F., Wang, Z. Y., & Huang, L. (2020). Using a one-dimensional convolutional neural network with a conditional generative adversarial network to classify plant electrical signals. Computers and Electronics in Agriculture, 174, 105464.
    [CrossRef] [Google Scholar]
  68. Bennett, C. M., Miller, M. B., & Wolford, G. L. (2009). Neural correlates of interspecies perspective taking in the post-mortem Atlantic Salmon: an argument for multiple comparisons correction. Neuroimage, 47(Suppl 1), S125. https://www.psychology.mcmaster.ca/bennett/psy710/readings/BennettDeadSalmon.pdf
    [Google Scholar]
  69. Poldrack, R. A., & Gorgolewski, K. J. (2014). Making big data open: data sharing in neuroimaging. Nature neuroscience, 17(11), 1510-1517.
    [CrossRef] [Google Scholar]
  70. Wong, S., Simmons, A., Rivera‐Villicana, J., Barnett, S., Sivathamboo, S., Perucca, P., ... & O'Brien, T. J. (2023). EEG datasets for seizure detection and prediction—A review. Epilepsia Open, 8(2), 252-267.
    [CrossRef] [Google Scholar]
  71. Delorme, A., & Makeig, S. (2004). EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of neuroscience methods, 134(1), 9-21.
    [CrossRef] [Google Scholar]
  72. Chen, J., Chen, J., Zhang, D., Sun, Y., & Nanehkaran, Y. A. (2020). Using deep transfer learning for image-based plant disease identification. Computers and electronics in agriculture, 173, 105393.
    [CrossRef] [Google Scholar]
  73. Huang, L., Peng, Z. J., Yao, J. P., Liu, J. J., Li, Y., Zheng, H. Y., & Wang, Z. Plant Electrical Signal Research Based on Physics-Informed Neural Networks. Available at SSRN 4385927. https://dx.doi.org/10.2139/ssrn.4385927
    [Google Scholar]
  74. Chang, C., & Chen, J. E. (2021). Multimodal EEG-fMRI: advancing insight into large-scale human brain dynamics. Current opinion in biomedical engineering, 18, 100279.
    [CrossRef] [Google Scholar]
  75. Codina, T., Blankertz, B., & Lühmann, A. V. (2025). Multimodal fNIRS–EEG sensor fusion: Review of data-driven methods and perspective for naturalistic brain imaging. Imaging Neuroscience, 3, IMAG-a.
    [CrossRef] [Google Scholar]
  76. Zhu, F., Zhang, J., Dang, R., Hu, B., & Wang, Q. (2025). MTNet: Multimodal transformer network for mild depression detection through fusion of EEG and eye tracking. Biomedical signal processing and control, 100, 106996.
    [CrossRef] [Google Scholar]
  77. Liu, H., Lou, T., Zhang, Y., Wu, Y., Xiao, Y., Jensen, C. S., & Zhang, D. (2024). EEG-based multimodal emotion recognition: A machine learning perspective. IEEE Transactions on Instrumentation and Measurement, 73, 1-29.
    [CrossRef] [Google Scholar]
  78. Banerji, S., Hõrak, H., Torop, J., & Huynh, T. P. (2024). Unravelling the secrets of plants: emerging wearable sensors for plants signals and physiology. Advanced Sensor Research, 3(3), 2300023.
    [CrossRef] [Google Scholar]
  79. Khanuja, S. P., Darokar, M. P., Mishra, S., Gangwar, A., Shasany, A. K., Kumar, T. S., ... & Kumar, S. (2001). Development of a novel plant system as biosensor for detecting environmental hazards and bioactive molecules through distinct responses. Journal of environmental pathology, toxicology and oncology, 20(1), 15–22.
    [CrossRef] [Google Scholar]
  80. Volkov, A. G., & Ranatunga, D. R. A. (2006). Plants as environmental biosensors. Plant signaling & behavior, 1(3), 105-115.
    [CrossRef] [Google Scholar]
  81. Al Harthy, S., Sadati, S. H., Girerd, C., Kim, S., Mondini, A., Wu, Z., ... & Bergeles, C. (2025). Tip-Growing Robots: Design, Theory, Application. IEEE transactions on robotics: a publication of the IEEE Robotics and Automation Society, 41, 5511-5532.
    [CrossRef] [Google Scholar]
  82. Blumenschein, L. H., Coad, M. M., Haggerty, D. A., Okamura, A. M., & Hawkes, E. W. (2020). Design, modeling, control, and application of everting vine robots. Frontiers in Robotics and AI, 7, 548266.
    [CrossRef] [Google Scholar]
  83. Del Dottore, E., Mondini, A., Rowe, N., & Mazzolai, B. (2024). A growing soft robot with climbing plant–inspired adaptive behaviors for navigation in unstructured environments. Science Robotics, 9(86), eadi5908.
    [CrossRef] [Google Scholar]

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APA Style
Wang, Z., Yang, F., Zhang, R., & Zhao, D. (2026). Neuroscience-Inspired Plant Electrophysiology: From Signal Decoding to Plant-Computer Interfaces. Journal of Plant Electrobiology, 1(2), 123-140. https://doi.org/10.62762/JPE.2026.744863
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TY  - JOUR
AU  - Wang, Ziyang
AU  - Yang, Fangmei
AU  - Zhang, Ruihang
AU  - Zhao, Dongjie
PY  - 2026
DA  - 2026/06/30
TI  - Neuroscience-Inspired Plant Electrophysiology: From Signal Decoding to Plant-Computer Interfaces
JO  - Journal of Plant Electrobiology
T2  - Journal of Plant Electrobiology
JF  - Journal of Plant Electrobiology
VL  - 1
IS  - 2
SP  - 123
EP  - 140
DO  - 10.62762/JPE.2026.744863
UR  - https://www.icck.org/article/abs/JPE.2026.744863
KW  - plant electrophysiology
KW  - plant-computer interface
KW  - multi-modal fusion
KW  - signal decoding
KW  - plant-in-the-loop
KW  - soft robotics
AB  - Plant electrophysiology is undergoing a profound paradigm shift from traditional phenomenological observation to systemic signal decoding, with mature methodologies from computational neuroscience and brain-computer interface technologies providing critical theoretical and engineering support for this interdisciplinary evolution. This review first systematically summarizes the evolution of flexible wearable electrodes and ultra-high impedance amplification hardware systems tailored to the ultra-slow signal dynamics and continuous morphological growth characteristics of plants. Second, we discuss the application pathways of introducing standardized sequential evoked paradigms from neuroscience—such as steady-state visual evoked potentials and event-related potentials—into the plant domain. This aims to replace traditional destructive stimuli with non-invasive, reproducible rhythmic stimulation to acquire data with high signal-to-noise ratios. In the dimension of data analysis, we explore modeling strategies that incorporate physics-informed neural networks and multi-modal heterogeneous sensor fusion technologies under the constraint of sample scarcity, aiming to resolve the equifinality problem inherent in single-modality electrical signal decoding. Building upon this decoding foundation, this paper proposes the construction of a bidirectional Plant-Computer Interface architecture, exploring the engineering feasibility of utilizing the plant itself as an active sensory node to directly drive closed-loop regulation within agricultural environments. Establishing cross-species standardized open-source datasets and unified hardware/software testing benchmarks will be the core driving force in overcoming current data fragmentation. Ultimately, the deep integration of multidisciplinary approaches will lay a rigorous scientific foundation for precision agricultural resource management and the development of next-generation bio-inspired intelligent hardware.
SN  - 3071-6268
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  -
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@article{Wang2026Neuroscien,
  author = {Ziyang Wang and Fangmei Yang and Ruihang Zhang and Dongjie Zhao},
  title = {Neuroscience-Inspired Plant Electrophysiology: From Signal Decoding to Plant-Computer Interfaces},
  journal = {Journal of Plant Electrobiology},
  year = {2026},
  volume = {1},
  number = {2},
  pages = {123-140},
  doi = {10.62762/JPE.2026.744863},
  url = {https://www.icck.org/article/abs/JPE.2026.744863},
  abstract = {Plant electrophysiology is undergoing a profound paradigm shift from traditional phenomenological observation to systemic signal decoding, with mature methodologies from computational neuroscience and brain-computer interface technologies providing critical theoretical and engineering support for this interdisciplinary evolution. This review first systematically summarizes the evolution of flexible wearable electrodes and ultra-high impedance amplification hardware systems tailored to the ultra-slow signal dynamics and continuous morphological growth characteristics of plants. Second, we discuss the application pathways of introducing standardized sequential evoked paradigms from neuroscience—such as steady-state visual evoked potentials and event-related potentials—into the plant domain. This aims to replace traditional destructive stimuli with non-invasive, reproducible rhythmic stimulation to acquire data with high signal-to-noise ratios. In the dimension of data analysis, we explore modeling strategies that incorporate physics-informed neural networks and multi-modal heterogeneous sensor fusion technologies under the constraint of sample scarcity, aiming to resolve the equifinality problem inherent in single-modality electrical signal decoding. Building upon this decoding foundation, this paper proposes the construction of a bidirectional Plant-Computer Interface architecture, exploring the engineering feasibility of utilizing the plant itself as an active sensory node to directly drive closed-loop regulation within agricultural environments. Establishing cross-species standardized open-source datasets and unified hardware/software testing benchmarks will be the core driving force in overcoming current data fragmentation. Ultimately, the deep integration of multidisciplinary approaches will lay a rigorous scientific foundation for precision agricultural resource management and the development of next-generation bio-inspired intelligent hardware.},
  keywords = {plant electrophysiology, plant-computer interface, multi-modal fusion, signal decoding, plant-in-the-loop, soft robotics},
  issn = {3071-6268},
  publisher = {Institute of Central Computation and Knowledge}
}

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Journal of Plant Electrobiology
Journal of Plant Electrobiology
ISSN: 3071-6268 (Online)
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