Dataset Information

A Protocol for Comparing Dry and Wet EEG Electrodes During Sleep.

ABSTRACT: Background:Sleep is commonly assessed by recording the electroencephalogram (EEG) of the sleeping brain. As sleep assessments in a lab environment are cumbersome for both the participant and researcher, it would be highly desirable to record sleep EEG with a user-friendly and mobile device. Dry electrodes that are reusable, low-cost, and easy to apply would be an essential component of such a device. In this study, we developed a testing protocol to investigate the performance of novel flat-type dry electrodes for sleep EEG recordings in free-living conditions. Methods:Overnight sleep EEG, electrooculogram and electromyogram of four young and healthy participants were recorded at home. Two identical ambulatory recording devices, one using novel flat-type dry electrodes, the other using self-adhesive pre-gelled electrodes, simultaneously recorded sleep EEG. Between both electrode types, we then compared the signal quality, the incidence of artifacts, the sensitivity, specificity and inter-scoring reliability (Cohen's kappa) of sleep staging, as well as the agreement of important characteristics of sleep-specific EEG microstructure features, such as slow waves (0.5-4 Hz) and sleep spindles (10-16 Hz). Results:Our testing protocol comprehensively compared the two electrode types on a macro- and microstructure level of sleep. The dry and pre-gelled electrodes both had comparable signal quality and sleep staging was feasible with both electrodes. Also, slow-wave and spindle characteristics were similar. However, sweat artifacts were more prevalent in the flat-type dry electrodes. Conclusion:With a reliable testing protocol, the performance of dry electrodes can be compared to reference technologies and objectively assessed also in free-living conditions.

SUBMITTER: Leach S

PROVIDER: S-EPMC7313551 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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A Protocol for Comparing Dry and Wet EEG Electrodes During Sleep.

Leach Sven S Chung Ku-Young KY Tüshaus Laura L Huber Reto R Karlen Walter W

Frontiers in neuroscience 20200617

<h4>Background</h4>Sleep is commonly assessed by recording the electroencephalogram (EEG) of the sleeping brain. As sleep assessments in a lab environment are cumbersome for both the participant and researcher, it would be highly desirable to record sleep EEG with a user-friendly and mobile device. Dry electrodes that are reusable, low-cost, and easy to apply would be an essential component of such a device. In this study, we developed a testing protocol to investigate the performance of novel f ...[more]

PMID: 32625053

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Project description: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.

Project description:BackgroundBrain computer interfaces (BCI) based on electro-encephalography (EEG) have been shown to detect mental states accurately and non-invasively, but the equipment required so far is cumbersome and the resulting signal is difficult to analyze. BCI requires accurate classification of small amplitude brain signal components in single trials from recordings which can be compromised by currents induced by muscle activity.Methodology/principal findingsA novel EEG cap based on dry electrodes was developed which does not need time-consuming gel application and uses far fewer electrodes than on a standard EEG cap set-up. After optimizing the placement of the 6 dry electrodes through off-line analysis of standard cap experiments, dry cap performance was tested in the context of a well established BCI cursor control paradigm in 5 healthy subjects using analysis methods which do not necessitate user training. The resulting information transfer rate was on average about 30% slower than the standard cap. The potential contribution of involuntary muscle activity artifact to the BCI control signal was found to be inconsequential, while the detected signal was consistent with brain activity originating near the motor cortex.Conclusions/significanceOur study shows that a surprisingly simple and convenient method of brain activity imaging is possible, and that simple and robust analysis techniques exist which discriminate among mental states in single trials. Within 15 minutes the dry BCI device is set-up, calibrated and ready to use. Peak performance matched reported EEG BCI state of the art in one subject. The results promise a practical non-invasive BCI solution for severely paralyzed patients, without the bottleneck of setup effort and limited recording duration that hampers current EEG recording technique. The presented recording method itself, BCI not considered, could significantly widen the use of EEG for emerging applications requiring long-term brain activity and mental state monitoring.

Project description:BackgroundEnriched language exposure may benefit infants in the neonatal intensive care unit. We hypothesized that changes in neonatal electroencephalogram (EEG) coherence during sleep, in response to maternal voice exposure, predict language development.MethodsConvalescent neonates underwent 12-h polysomnography. A recording of the mother's voice was randomized to continuous playback in the first or second 6 h. We calculated the imaginary coherence (ICOH-a measure of functional connectivity) between EEG leads. Spearman correlations were computed between ICOH and 18-month Bayley-III language scores.ResultsThirty-five neonates were included (N = 18 33-to-<35 weeks gestation; N = 17 ≥ 35 weeks). Predictive value of ICOH during neonatal non-rapid eye movement (NREM) sleep was left lateralized, and varied with gestational age and voice playback. ICOH in the left-hemispheric (C3-Cz; T3-Cz) channels across multiple EEG frequency bands was associated with 18-month language scores (rho = -0.34 to -0.48). The association was driven by neonates born at 33-34 weeks gestation, and a trend suggested a possible effect of maternal voice at some EEG frequencies. Right hemisphere ICOH (C4-Cz; T4-Cz) was not associated with language outcome.ConclusionsLeft-hemispheric EEG functional connectivity during neonatal NREM sleep shows early signs of physiologic asymmetry that may predict language development. We speculate that sleep analyses could have unique prognostic value.ImpactDuring neonatal NREM sleep, EEG functional connectivity predicts future language development. Left temporal and central EEG coherence-specifically the imaginary component of coherence-is predictive, whereas the same analysis from the right hemisphere is not. These results appear to vary according to the infant's gestational age, and a trend suggests they may be enhanced by measuring functional connectivity during exposure to the mother's voice. These findings identify early evidence of physiologic differentiation within the cerebral hemispheres and raise the possibility that neonatal NREM sleep has a role to play in language development.

Dataset Information

A Protocol for Comparing Dry and Wet EEG Electrodes During Sleep.

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A Protocol for Comparing Dry and Wet EEG Electrodes During Sleep.

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