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

Meihong Zhang1,2, Bocheng Qian3, Jianming Gao4

  • 1School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China.

Sensors (Basel, Switzerland)
|August 28, 2025
PubMed
Summary

Dry electrode systems for brain-computer interfaces (BCI) are advancing rapidly. This review details hardware innovations and applications like emotion recognition, offering a roadmap for next-generation EEG systems.

Keywords:
BCIEEGartificial intelligencedry electrodesemotion recognitionfatigue detectionmotor imagerysteady-state visual evoked potentials

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Area of Science:

  • Neuroscience and Biomedical Engineering
  • Focus on brain-computer interface (BCI) systems and electroencephalography (EEG) signal acquisition.

Background:

  • Traditional EEG systems require conductive gel, limiting real-world applications.
  • Advancements in BCI necessitate electrode systems with improved electrical performance, comfort, and portability.

Purpose of the Study:

  • To review recent progress in dry electrode EEG systems for BCI.
  • To summarize hardware innovations, material developments, and application advances.
  • To identify challenges and future research directions.

Main Methods:

  • Comprehensive literature review of dry electrode EEG systems.
  • Analysis of hardware design (structural innovation, materials).
  • Examination of BCI applications (emotion recognition, fatigue detection, motor imagery, SSVEP).

Main Results:

  • Dry electrodes offer a gel-free alternative for EEG acquisition.
  • Significant progress in structural and material innovations for dry electrodes.
  • Demonstrated BCI applications in various domains show system-level performance gains.

Conclusions:

  • Dry electrode EEG systems are crucial for practical BCI implementation.
  • Key future priorities include standardized evaluation, enhanced generalization, and hardware-algorithm optimization.
  • This review provides a reference for developing next-generation dry electrode EEG-BCI systems.