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Review
. 2025 Aug 21;25(16):5215.
doi: 10.3390/s25165215.

Recent Advances in Portable Dry Electrode EEG: Architecture and Applications in Brain-Computer Interfaces

Meihong Zhang  1   2 , Bocheng Qian  3 , Jianming Gao  4 , Shaokai Zhao  2 , Yibo Cui  2 , Zhiguo Luo  2 , Kecheng Shi  2 , Erwei Yin  2
Affiliations
Review

Recent Advances in Portable Dry Electrode EEG: Architecture and Applications in Brain-Computer Interfaces

Meihong Zhang et al. Sensors (Basel). .

Abstract

As brain-computer interface (BCI) technology continues to advance, research on human brain function has gradually transitioned from theoretical investigation to practical engineering applications. To support EEG signal acquisition in a variety of real-world scenarios, BCI electrode systems must demonstrate a balanced combination of electrical performance, wearing comfort, and portability. Dry electrodes have emerged as a promising alternative for EEG acquisition due to their ability to operate without conductive gel or complex skin preparation. This paper reviews the latest progress in dry electrode EEG systems, summarizing key achievements in hardware design with a focus on structural innovation and material development. It also examines application advances in several representative BCI domains, including emotion recognition, fatigue and drowsiness detection, motor imagery, and steady-state visual evoked potentials, while analyzing system-level performance. Finally, the paper critically assesses existing challenges and identifies critical future research priorities. Key recommendations include developing a standardized evaluation framework to bolster research reliability, enhancing generalization performance, and fostering coordinated hardware-algorithm optimization. These steps are crucial for advancing the practical implementation of these technologies across diverse scenarios. With this survey, we aim to offer a comprehensive reference and roadmap for researchers engaged in the development and implementation of next-generation dry electrode EEG-based BCI systems.

Keywords: BCI; EEG; artificial intelligence; dry electrodes; emotion recognition; fatigue detection; motor imagery; steady-state visual evoked potentials.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Figure 1
Figure 1
Examples of microneedle array dry electrodes. (a) Silicon microneedle-based dry electrode (reprinted with permission from [26], © 2013 Elsevier). (b) Polymer microneedle electrodes (reprinted with permission from [48], © The Japan Society of Applied Physics). (c) Flexible microneedle array electrode [49] (reprinted under CC-BY license). (d) SU-8 microneedle electrodes (reprinted with permission from [36], © 2016 Elsevier).
Figure 3
Figure 3
Examples of dry contact electrodes. (a) Claw-shaped electrodes [59] (reprinted under CC-BY license). (b) Claw-shaped electrodes [60] (reprinted under CC-BY license). (c) Pin-shaped electrodes (reprinted with permission from [63], © 2018 Elsevier). (d) Finger-shaped electrodes [62] (reprinted under CC-BY license). (e) The clutter-free e-textile EEG cap (reprinted with permission from [68], © 2025 Elsevier).
Figure 2
Figure 2
Example of dry non-contact electrodes [51], reprinted under CC-BY license.
Figure 4
Figure 4
The key applications of BCI.
See this image and copyright information in PMC

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References

    1. Wang Y., Wang J., Wang W., Su J., Bunterngchit C., Hou Z.G. TFTL: A task-free transfer learning strategy for EEG-based cross-subject & cross-dataset motor imagery BCI. IEEE Trans. Biomed. Eng. 2024;72:810–821. - PubMed
    1. Kilmarx J., Tashev I., Millán J.d.R., Sulzer J., Lewis-Peacock J. Evaluating the feasibility of visual imagery for an EEG-based brain-computer interface. IEEE Trans. Neural Syst. Rehabil. Eng. 2024;32:2209–2219. doi: 10.1109/TNSRE.2024.3410870. - DOI - PMC - PubMed
    1. Shi X., Li B., Wang W., Qin Y., Wang H., Wang X. EEG-VTTCNet: A loss joint training model based on the vision transformer and the temporal convolution network for EEG-based motor imagery classification. Neuroscience. 2024;556:42–51. doi: 10.1016/j.neuroscience.2024.07.051. - DOI - PubMed
    1. Ngo T.D., Kieu H.D., Nguyen M.H., Nguyen T.H.A., Can V.M., Nguyen B.H., Le T.H. An EEG & eye-tracking dataset of ALS patients & healthy people during eye-tracking-based spelling system usage. Sci. Data. 2024;11:664. - PMC - PubMed
    1. Lee M.H., Williamson J., Won D.O., Fazli S., Lee S.W. A high performance spelling system based on EEG-EOG signals with visual feedback. IEEE Trans. Neural Syst. Rehabil. Eng. 2018;26:1443–1459. doi: 10.1109/TNSRE.2018.2839116. - DOI - PubMed

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