Research progress on flexible electrode technology in brain computer interface applications_Journal of Biomedical Engineering

Authors：

LAI Zixi , FENG Danqian , LIANG Meishuang , LIANG Weitao , XU Yuchun ,  KE Jun

Guangdong Medical Device Quality Supervision and Inspection Institute, Guangzhou 510663, P. R. China;

Corresponding?author：

Keywords：

Brain computer interface; Flexible electrodes; Bio-compatibility; Neural engineering

DOI：

10.7507/1001-5515.202508066

Video：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

Flexible electrode as a revolutionary brain computer interface (BCI) technology in the field of neural engineering, has achieved high-fidelity acquisition and long-term stable transmission of electroencephalographic signals through their exceptional bio-compatibility. This review systematically elucidates the design paradigms and material innovation systems of flexible electrodes, focusing on their transitional medical value from aspects such as electrode materials, signal acquisition and processing. It identifies the current technical bottlenecks that urgently need to be broken through and outlines the future development directions, hoping to provide a systematic technical road-map and evaluation framework for the technical development of next-generation BCI.

Citation： LAI Zixi, FENG Danqian, LIANG Meishuang, LIANG Weitao, XU Yuchun, KE Jun. Research progress on flexible electrode technology in brain computer interface applications. Journal of Biomedical Engineering, 2026, 43(1): 186-192. doi: 10.7507/1001-5515.202508066 Copy

1.	Zhai P, Xuan X, Li H, et al. Boron and nitrogen co-doped vertical graphene electrodes for scalp electroencephalogram recording. Carbon, 2022, 189: 71-80.
2.	Xu Z, Khazaee M, Truong N D, et al. A leadless power transfer and wireless telemetry solutions for an endovascular electrocorticography. Journal of Neural Engineering, 2024, 21(6): 066009.
3.	Ozturk M, Telkes I, Jimenez-Shahed J, et al. Randomized, double-blind assessment of LFP versus SUA guidance in STN-DBS lead implantation: a pilot study. Frontiers in Neuroscience, 2020, 14: 611.
4.	Siribunyaphat N, Punsawad Y. Brain-computer interface based on steady-state visual evoked potential using quick-response code pattern for wheelchair control. Sensors (Basel), 2023, 23(4): 2069.
5.	Luo J, Xue N, Chen J. A review: research progress of neural probes for brain research and brain-computer interface. Biosensors (Basel), 2022, 12(12): 1167.
6.	Jiao Y, Lei M, Zhu J, et al. Advances in electrode interface materials and modification technologies for brain-computer interfaces. Biomater Transl, 2023, 4(4): 213-233.
7.	Dong R, Wang L, Li Z, et al. Stretchable, self-rolled, microfluidic electronics enable conformable neural interfaces of brain and vagus neuromodulation. ACS Nano, 2024, 18(2): 1702-1713.
8.	Lin Z, Marin-llobet A, Baek J, et al. Spike sorting AI agent. bioRxiv, 2025, DOI: 10.1101/2025.02.11.637754.
9.	Merken L, Schelles M, Ceyssens F, et al. Thin flexible arrays for long-term multi-electrode recordings in macaque primary visual cortex. Journal of Neural Engineering, 2022, 19(6): 066039.
10.	Apollo N V, Murphy B, Prezelski K, et al. Gels, jets, mosquitoes, and magnets: a review of implantation strategies for soft neural probes. Journal of Neural Engineering, 2020, 17(4): 041002.
11.	Tang C, Wang P, Li Z, et al. Neural functional rehabilitation: exploring neuromuscular reconstruction technology advancements and challenges. Neural Regeneration Research, 2026, 21(1): 173-186.
12.	Davis K C, Meschede-krasa B, Cajigas I, et al. Design-development of an at-home modular brain-computer interface (BCI) platform in a case study of cervical spinal cord injury. Journal of NeuroEngineering and Rehabilitation, 2022, 19(1): 53.
13.	Peksa J, Mamchur D. State-of-the-art on brain-computer interface technology. Sensors (Basel), 2023, 23(13): 6001.
14.	Waisberg E, Ong J, Lee A G. Ethical considerations of neuralink and brain-computer interfaces. Ann Biomed Eng, 2024, 52(8): 1937-1939.
15.	Edelman B J, Zhang S, Schalk G, et al. Non-invasive brain-computer interfaces: state of the art and trends. IEEE Rev Biomed Eng, 2025, 18: 26-49.
16.	Duan Y, Wang S, Yuan Q, et al. Long-term flexible neural interface for synchronous recording of cross-regional sensory processing along the olfactory pathway. Small, 2023, 19(29): e2205768.
17.	Liu X Y, Wang W L, Liu M, et al. Recent applications of EEG-based brain-computer-interface in the medical field. Mil Med Res, 2025, 12(1): 14.
18.	Rosenfeld J V. Neurosurgery and the brain-computer interface. Adv Exp Med Biol, 2024, 1462: 513-527.
19.	Tang J, Lebel A, Jain S, et al. Semantic reconstruction of continuous language from non-invasive brain recordings. Nat Neurosci, 2023, 26(5): 858-866.
20.	Xiang Z, Yen S C, Xue N, et al. Ultra-thin flexible polyimide neural probe embedded in a dissolvable maltose-coated microneedle. Journal of Micromechanics and Microengineering, 2014, 24(6): 065015.
21.	Li J, Wang F, Huang H, et al. A novel semi-supervised meta learning method for subject-transfer brain-computer interface. Neural Netw, 2023, 163: 195-204.
22.	Ganji M, Kaestner E, Hermiz J, et al. Development and translation of PEDOT: PSS microelectrodes for intraoperative monitoring. Advanced Functional Materials, 2018, 28(12): 1700232.
23.	Chen N, Luo B, Patil A C, et al. Nanotunnels within poly(3,4-ethylenedioxythiophene)-carbon nanotube composite for highly sensitive neural interfacing. Acs Nano, 2020, 14(7): 8059-8073.
24.	Zhu M, Wang H, Li S, et al. Flexible electrodes for in vivo and in vitro electrophysiological signal recording. Advanced Healthcare Materials, 2021, 10(17): e2100646.
25.	Zabcikova M, Koudelkova Z, Jasek R, et al. Recent advances and current trends in brain-computer interface research and their applications. Int J Dev Neurosci, 2022, 82(2): 107-123.
26.	Fiedler P, Fonseca C, Supriyanto E, et al. A high-density 256-channel cap for dry electroencephalography. Human Brain Mapping, 2022, 43(4): 1295-1308.
27.	Liu X, Liu J, Lin S, et al. Hydrogel machines. Materials Today, 2020, 36: 102-124.
28.	Canny E, Vansteensel M J, van der Salm S M A, et al. Boosting brain-computer interfaces with functional electrical stimulation: potential applications in people with locked-in syndrome. J Neuroeng Rehabil, 2023, 20(1): 157.
29.	Li R, Zhao X, Wang Z, et al. A novel hybrid brain-computer interface combining the illusion-induced VEP and SSVEP. IEEE Trans Neural Syst Rehabil Eng, 2023, 31: 4760-4772.
30.	Vansteensel M J, Branco M P, Leinders S, et al. Methodological recommendations for studies on the daily life implementation of implantable communication-brain-computer interfaces for individuals with locked-in syndrome. Neurorehabilitation and Neural Repair, 2022, 36(10-11): 666-677.
31.	Zheng Y Q, Liu Y, Zhong D, et al. Monolithic optical microlithography of high-density elastic circuits. Science, 2021, 373(6550): 88-94.
32.	Ran X, Chen W, Yvert B, et al. A hybrid autoencoder framework of dimensionality reduction for brain-computer interface decoding. Comput Biol Med, 2022, 148: 105871.
33.	Henderson F C, Tuchman K. Angiogenic cell precursors and neural cell precursors in service to the brain-computer interface. Cells, 2025, 14(15): 1163.
34.	Siviero I, Menegaz G, Storti S F. Functional connectivity and feature fusion enhance multiclass motor-imagery brain-computer interface performance. Sensors (Basel), 2023, 23(17): 7520.
35.	Kim M G, Lim H, Lee H S, et al. Brain-computer interface-based action observation combined with peripheral electrical stimulation enhances corticospinal excitability in healthy subjects and stroke patients. J Neural Eng, 2022, 19(3): 036039.
36.	Awuah W A, Ahluwalia A, Darko K, et al. Bridging minds and machines: the recent advances of brain-computer interfaces in neurological and neurosurgical applications. World Neurosurg, 2024, 189: 138-153.
37.	Palumbo A, Ielpo N, Calabrese B. An FPGA-embedded brain-computer interface system to support individual autonomy in locked-in individuals. Sensors (Basel), 2022, 22(1): 318.
38.	Caria A. Towards predictive communication: the fusion of large language models and brain-computer interface. Sensors (Basel), 2025, 25(13): 3987.
39.	Gok S, Sahin M. Prediction of forelimb EMGs and movement phases from corticospinal signals in the rat during the reach-to-pull task. International Journal of Neural Systems, 2019, 29(7): 1950009.
40.	Chen Y, Peng Y, Tang J, et al. EEG-based affective brain-computer interfaces: recent advancements and future challenges. J Neural Eng, 2025, 22(3): 031004.
41.	Rajpura P, Cecotti H, Kumar Meena Y. Explainable artificial intelligence approaches for brain-computer interfaces: a review and design space. J Neural Eng, 2024, 21(4): 041003.

1. Zhai P, Xuan X, Li H, et al. Boron and nitrogen co-doped vertical graphene electrodes for scalp electroencephalogram recording. Carbon, 2022, 189: 71-80.
2. Xu Z, Khazaee M, Truong N D, et al. A leadless power transfer and wireless telemetry solutions for an endovascular electrocorticography. Journal of Neural Engineering, 2024, 21(6): 066009.
3. Ozturk M, Telkes I, Jimenez-Shahed J, et al. Randomized, double-blind assessment of LFP versus SUA guidance in STN-DBS lead implantation: a pilot study. Frontiers in Neuroscience, 2020, 14: 611.
4. Siribunyaphat N, Punsawad Y. Brain-computer interface based on steady-state visual evoked potential using quick-response code pattern for wheelchair control. Sensors (Basel), 2023, 23(4): 2069.
5. Luo J, Xue N, Chen J. A review: research progress of neural probes for brain research and brain-computer interface. Biosensors (Basel), 2022, 12(12): 1167.
6. Jiao Y, Lei M, Zhu J, et al. Advances in electrode interface materials and modification technologies for brain-computer interfaces. Biomater Transl, 2023, 4(4): 213-233.
7. Dong R, Wang L, Li Z, et al. Stretchable, self-rolled, microfluidic electronics enable conformable neural interfaces of brain and vagus neuromodulation. ACS Nano, 2024, 18(2): 1702-1713.
8. Lin Z, Marin-llobet A, Baek J, et al. Spike sorting AI agent. bioRxiv, 2025, DOI: 10.1101/2025.02.11.637754.
9. Merken L, Schelles M, Ceyssens F, et al. Thin flexible arrays for long-term multi-electrode recordings in macaque primary visual cortex. Journal of Neural Engineering, 2022, 19(6): 066039.
10. Apollo N V, Murphy B, Prezelski K, et al. Gels, jets, mosquitoes, and magnets: a review of implantation strategies for soft neural probes. Journal of Neural Engineering, 2020, 17(4): 041002.
11. Tang C, Wang P, Li Z, et al. Neural functional rehabilitation: exploring neuromuscular reconstruction technology advancements and challenges. Neural Regeneration Research, 2026, 21(1): 173-186.
12. Davis K C, Meschede-krasa B, Cajigas I, et al. Design-development of an at-home modular brain-computer interface (BCI) platform in a case study of cervical spinal cord injury. Journal of NeuroEngineering and Rehabilitation, 2022, 19(1): 53.
13. Peksa J, Mamchur D. State-of-the-art on brain-computer interface technology. Sensors (Basel), 2023, 23(13): 6001.
14. Waisberg E, Ong J, Lee A G. Ethical considerations of neuralink and brain-computer interfaces. Ann Biomed Eng, 2024, 52(8): 1937-1939.
15. Edelman B J, Zhang S, Schalk G, et al. Non-invasive brain-computer interfaces: state of the art and trends. IEEE Rev Biomed Eng, 2025, 18: 26-49.
16. Duan Y, Wang S, Yuan Q, et al. Long-term flexible neural interface for synchronous recording of cross-regional sensory processing along the olfactory pathway. Small, 2023, 19(29): e2205768.
17. Liu X Y, Wang W L, Liu M, et al. Recent applications of EEG-based brain-computer-interface in the medical field. Mil Med Res, 2025, 12(1): 14.
18. Rosenfeld J V. Neurosurgery and the brain-computer interface. Adv Exp Med Biol, 2024, 1462: 513-527.
19. Tang J, Lebel A, Jain S, et al. Semantic reconstruction of continuous language from non-invasive brain recordings. Nat Neurosci, 2023, 26(5): 858-866.
20. Xiang Z, Yen S C, Xue N, et al. Ultra-thin flexible polyimide neural probe embedded in a dissolvable maltose-coated microneedle. Journal of Micromechanics and Microengineering, 2014, 24(6): 065015.
21. Li J, Wang F, Huang H, et al. A novel semi-supervised meta learning method for subject-transfer brain-computer interface. Neural Netw, 2023, 163: 195-204.
22. Ganji M, Kaestner E, Hermiz J, et al. Development and translation of PEDOT: PSS microelectrodes for intraoperative monitoring. Advanced Functional Materials, 2018, 28(12): 1700232.
23. Chen N, Luo B, Patil A C, et al. Nanotunnels within poly(3,4-ethylenedioxythiophene)-carbon nanotube composite for highly sensitive neural interfacing. Acs Nano, 2020, 14(7): 8059-8073.
24. Zhu M, Wang H, Li S, et al. Flexible electrodes for in vivo and in vitro electrophysiological signal recording. Advanced Healthcare Materials, 2021, 10(17): e2100646.
25. Zabcikova M, Koudelkova Z, Jasek R, et al. Recent advances and current trends in brain-computer interface research and their applications. Int J Dev Neurosci, 2022, 82(2): 107-123.
26. Fiedler P, Fonseca C, Supriyanto E, et al. A high-density 256-channel cap for dry electroencephalography. Human Brain Mapping, 2022, 43(4): 1295-1308.
27. Liu X, Liu J, Lin S, et al. Hydrogel machines. Materials Today, 2020, 36: 102-124.
28. Canny E, Vansteensel M J, van der Salm S M A, et al. Boosting brain-computer interfaces with functional electrical stimulation: potential applications in people with locked-in syndrome. J Neuroeng Rehabil, 2023, 20(1): 157.
29. Li R, Zhao X, Wang Z, et al. A novel hybrid brain-computer interface combining the illusion-induced VEP and SSVEP. IEEE Trans Neural Syst Rehabil Eng, 2023, 31: 4760-4772.
30. Vansteensel M J, Branco M P, Leinders S, et al. Methodological recommendations for studies on the daily life implementation of implantable communication-brain-computer interfaces for individuals with locked-in syndrome. Neurorehabilitation and Neural Repair, 2022, 36(10-11): 666-677.
31. Zheng Y Q, Liu Y, Zhong D, et al. Monolithic optical microlithography of high-density elastic circuits. Science, 2021, 373(6550): 88-94.
32. Ran X, Chen W, Yvert B, et al. A hybrid autoencoder framework of dimensionality reduction for brain-computer interface decoding. Comput Biol Med, 2022, 148: 105871.
33. Henderson F C, Tuchman K. Angiogenic cell precursors and neural cell precursors in service to the brain-computer interface. Cells, 2025, 14(15): 1163.
34. Siviero I, Menegaz G, Storti S F. Functional connectivity and feature fusion enhance multiclass motor-imagery brain-computer interface performance. Sensors (Basel), 2023, 23(17): 7520.
35. Kim M G, Lim H, Lee H S, et al. Brain-computer interface-based action observation combined with peripheral electrical stimulation enhances corticospinal excitability in healthy subjects and stroke patients. J Neural Eng, 2022, 19(3): 036039.
36. Awuah W A, Ahluwalia A, Darko K, et al. Bridging minds and machines: the recent advances of brain-computer interfaces in neurological and neurosurgical applications. World Neurosurg, 2024, 189: 138-153.
37. Palumbo A, Ielpo N, Calabrese B. An FPGA-embedded brain-computer interface system to support individual autonomy in locked-in individuals. Sensors (Basel), 2022, 22(1): 318.
38. Caria A. Towards predictive communication: the fusion of large language models and brain-computer interface. Sensors (Basel), 2025, 25(13): 3987.
39. Gok S, Sahin M. Prediction of forelimb EMGs and movement phases from corticospinal signals in the rat during the reach-to-pull task. International Journal of Neural Systems, 2019, 29(7): 1950009.
40. Chen Y, Peng Y, Tang J, et al. EEG-based affective brain-computer interfaces: recent advancements and future challenges. J Neural Eng, 2025, 22(3): 031004.
41. Rajpura P, Cecotti H, Kumar Meena Y. Explainable artificial intelligence approaches for brain-computer interfaces: a review and design space. J Neural Eng, 2024, 21(4): 041003.

Journal of Biomedical Engineering

Research progress on flexible electrode technology in brain computer interface applications

Abstract Full text Figures/Tables Video References Cited by

Previous Article

Next Article

Format

Content