Evidence map›Paper›PMID 40872076›Full record

ReviewSensors (Basel, Switzerland)2025

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

Meihong Zhang, Bocheng Qian, Jianming Gao, Shaokai Zhao, Yibo Cui, Zhiguo Luo, Kecheng Shi, Erwei Yin

Abstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed.

  1. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Meihong ZhangSchool of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China.ORCID 0009-0005-1825-840X
Bocheng QianSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.ORCID 0009-0006-2935-1623
Jianming GaoAcademy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China.
Shaokai ZhaoDefense Innovation Institute, Academy of Military Sciences (AMS), Beijing 100071, China.
Yibo CuiDefense Innovation Institute, Academy of Military Sciences (AMS), Beijing 100071, China.
Zhiguo LuoDefense Innovation Institute, Academy of Military Sciences (AMS), Beijing 100071, China.
Kecheng ShiDefense Innovation Institute, Academy of Military Sciences (AMS), Beijing 100071, China.
Erwei YinDefense Innovation Institute, Academy of Military Sciences (AMS), Beijing 100071, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

BrainBrain-Computer InterfacesElectroencephalographyAlgorithmsElectrodesHumansSignal Processing, Computer-Assistedartificial intelligenceBCIdry electrodesEEGemotion recognitionfatigue detectionmotor imagerysteady-state visual evoked potentials

Identifiers

PMID40872076
PMCPMC12389868

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.