Literature DB >> 33002886

Review of semi-dry electrodes for EEG recording.

Guang-Li Li1, Jing-Tao Wu1, Yong-Hui Xia2, Quan-Guo He1, Hong-Guang Jin3.   

Abstract

Developing reliable and user-friendly electroencephalography (EEG) electrodes remains a challenge for emerging real-world EEG applications. Classic wet electrodes are the gold standard for recording EEG; however, they are difficult to implement and make users uncomfortable, thus severely restricting their widespread application in real-life scenarios. An alternative is dry electrodes, which do not require conductive gels or skin preparation. Despite their quick setup and improved user-friendliness, dry electrodes still have some inherent problems (invasive, relatively poor signal quality, or sensitivity to motion artifacts), which limit their practical utilization. In recent years, semi-dry electrodes, which require only a small amount of electrolyte fluid, have been successfully developed, combining the advantages of both wet and dry electrodes while addressing their respective drawbacks. Semi-dry electrodes can collect reliable EEG signals comparable to wet electrodes. Moreover, their setup is as fast and convenient similar to that of dry electrodes. Hence, semi-dry electrodes have shown tremendous application prospects for real-world EEG acquisition. Herein, we systematically summarize the development, evaluation methods, and practical design considerations of semi-dry electrodes. Some feasible suggestions and new ideas for the development of semi-dry electrodes have been presented. This review provides valuable technical support for the development of semi-dry electrodes toward emerging practical applications.

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Year:  2020        PMID: 33002886     DOI: 10.1088/1741-2552/abbd50

Source DB:  PubMed          Journal:  J Neural Eng        ISSN: 1741-2552            Impact factor:   5.379


  14 in total

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3.  Proposals and Comparisons from One-Sensor EEG and EOG Human-Machine Interfaces.

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Journal:  Sensors (Basel)       Date:  2021-03-22       Impact factor: 3.576

4.  CNN-Based Personal Identification System Using Resting State Electroencephalography.

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Journal:  Comput Intell Neurosci       Date:  2021-12-13

5.  Me-Doped Ti-Me Intermetallic Thin Films Used for Dry Biopotential Electrodes: A Comparative Case Study.

Authors:  Cláudia Lopes; Patrique Fiedler; Marco Sampaio Rodrigues; Joel Borges; Maurizio Bertollo; Eduardo Alves; Nuno Pessoa Barradas; Silvia Comani; Jens Haueisen; Filipe Vaz
Journal:  Sensors (Basel)       Date:  2021-12-06       Impact factor: 3.576

6.  A high-density 256-channel cap for dry electroencephalography.

Authors:  Patrique Fiedler; Carlos Fonseca; Eko Supriyanto; Frank Zanow; Jens Haueisen
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7.  A Deep Learning Strategy for Automatic Sleep Staging Based on Two-Channel EEG Headband Data.

Authors:  Amelia A Casciola; Sebastiano K Carlucci; Brianne A Kent; Amanda M Punch; Michael A Muszynski; Daniel Zhou; Alireza Kazemi; Maryam S Mirian; Jason Valerio; Martin J McKeown; Haakon B Nygaard
Journal:  Sensors (Basel)       Date:  2021-05-11       Impact factor: 3.576

Review 8.  Mind the gap: State-of-the-art technologies and applications for EEG-based brain-computer interfaces.

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Journal:  APL Bioeng       Date:  2021-07-20

9.  Effect of Static Posture on Online Performance of P300-Based BCIs for TV Control.

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Journal:  Sensors (Basel)       Date:  2021-03-24       Impact factor: 3.576

10.  Signal Quality Investigation of a New Wearable Frontal Lobe EEG Device.

Authors:  Zhilin Gao; Xingran Cui; Wang Wan; Zeguang Qin; Zhongze Gu
Journal:  Sensors (Basel)       Date:  2022-02-28       Impact factor: 3.576

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