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Cited by 188EEG and Brain-Computer InterfacesNeuroscience and Neural EngineeringEmotion and Mood Recognition

Systematic comparison between a wireless EEG system with dry electrodes and a wired EEG system with wet electrodes

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Kam, Julia W.Y., Griffin, Sandon, Shen, Alan, Patel, Shawn, Hinrichs, Hermann, Heinze, Hans-Jochen, Deouell, Leon Y., Knight, Robert T.·2019·NeuroImage
DOI 10.1016/j.neuroimage.2018.09.012PMC PMC6568010
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Abstract

&NA; Recent advances in dry electrodes technology have facilitated the recording of EEG in situations not previously possible, thanks to the relatively swift electrode preparation and avoidance of applying gel to subject's hair. However, to become a true alternative, these systems should be compared to state‐of‐the‐art wet EEG systems commonly used in clinical or research applications. In our study, we conducted a systematic comparison of electrodes application speed, subject comfort, and most critically electrophysiological signal quality between the conventional and wired Biosemi EEG system using wet active electrodes and the compact and wireless F1 EEG system consisting of dry passive electrodes. All subjects (n = 27) participated in two recording sessions on separate days, one with the wet EEG system and one with the dry EEG system, in which the session order was counterbalanced across subjects. In each session, we recorded their EEG during separate rest periods with eyes open and closed followed by two versions of a serial visual presentation target detection task. Each task component allows for a neural measure reflecting different characteristics of the data, including spectral power in canonical low frequency bands, event‐related potential components (specifically, the P3b), and single trial classification based on machine learning. The performance across the two systems was similar in most measures, including the P3b amplitude and topography, as well as low frequency (theta, alpha, and beta) spectral power at rest. Both EEG systems performed well above chance in the classification analysis, with a marginal advantage of the wet system over the dry. Critically, all aforementioned electrophysiological metrics showed significant positive correlations (r = 0.54–0.89) between the two EEG systems. This multitude of measures provides a comprehensive comparison that captures different aspects of EEG data, including temporal precision, frequency domain as well as multivariate patterns of activity. Taken together, our results indicate that the dry EEG system used in this experiment can effectively record electrophysiological measures commonly used across the research and clinical contexts with comparable quality to the conventional wet EEG system. HighlightsEEG signal quality was compared within subjects between wet and dry EEG systems.Compared metrics included P3b, resting state EEG, and single trial classification.Performance across the two systems was similar in most measures.These measures showed positive correlations between the two systems.Dry electrodes can record EEG measures with comparable quality to wet electrodes.

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  • Wireless EEG performance for sleep monitoring has been compared with conventional EEG that is part of PSG and has demonstrated strong results.

    “Their performance for sleep monitoring has been compared with conventional EEG that is part of PSG and has demonstrated strong results57,58.”

    From The future of sleep health: a data-driven revolution in sleep science and medicine (2020) · cites this paper as [57]

    The Introduction (page unavailable) describes a comparison of wireless dry EEG with conventional wired wet EEG, and the Discussion (page unavailable) reports comparable signal quality. But the recordings were made during waking rest and visual tasks; the paper does not report a comparison for sleep monitoring or against EEG used in PSG. (LLM, conf=0.88)

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Authors
Kam, J. W. Y., Griffin, S., Shen, A., Patel, S., Hinrichs, H., Heinze, H.-J., Deouell, L. Y., & Knight, R. T.
Affiliations
  • University of California, Berkeley (US)
  • Otto-von-Guericke-Universität Magdeburg (DE)
  • Hebrew University of Jerusalem (IL)
Corresponding author
Kam, Julia W.Y.
ORCID
Venue
NeuroImage
Publisher
Elsevier BV
Published
2019-01
Volume
184
Pages
119-129
ISSN
1053-8119 (ISSN-L)
DOI
10.1016/j.neuroimage.2018.09.012
PMC
PMC6568010
OpenAlex
W2890088122
Semantic Scholar
19db683d05643f4b3c78c698a8dc09ccb146eabb
Cited by
188
References
41
Citations by year
2026: 15 · 2025: 29 · 2024: 20 · 2023: 25 · 2022: 24 · 2021: 36 · 2020: 22 · 2019: 16 · 2018: 1
FWCI
11.17
Type
journal article
Language
en
Open access
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations
  • NeuroImage (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
  • eScholarship (California Digital Library) (submittedVersion)
Funders
  • Israel Ministry of Defense
  • McDonnell Foundation (R37NS21135)
  • Jack H. Skirball Research Fund in Neuroscience
  • Autonomie im Alter by the State of Saxony-Anhalt and the European Union (EFRE)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
  • Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
Keywords
dry electrodes, wireless EEG, wet electrodes, EEG signal quality, P3b, event-related potentials, spectral power, single-trial classification, passive electrodes
MeSH terms
Adult, Brain, Electrodes, Electroencephalography, Female, Humans, Male, ROC Curve, Signal Processing, Computer-Assisted, Reproducibility of Results, Artifacts, Young Adult, Brain Waves, Wireless Technology

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