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.
Bibliographical noteFunding Information:
We would like to thank the time and effort put forward by our participants. We also thank Alejandro Blenkmann for useful discussions, and Dragan Zivkovic, Marko Jovanovic, Michael Kim, Sergey Vaisman and Yuval Harpaz for technical support. This study was supported by the Israel Ministry of Defense to RTK, the McDonnell Foundation to RTK, NIH grant R37NS21135 to RTK, the Jack H. Skirball Research Fund in Neuroscience to LYD, and the Autonomie im Alter by the State of Saxony-Anhalt and the European Union (EFRE) to HH and HJH. The F1 dry EEG system was provided by Nielsen Corporation, and the classification algorithm by Innereye, Inc.
© 2018 Elsevier Inc.
- Dry electrodes
- Resting state EEG
- Single trial classification
- Wet electrodes