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

Kam, Julia W. Y.; Griffin, Sandon; Shen, Alan; Patel, Shawn; Hinrichs, Hermann; Heinze, Hans‐Jochen; Deouell, Leon Y.; Knight, Robert T.

doi:10.1016/j.neuroimage.2018.09.012

Cited by 145 publications

(149 citation statements)

References 40 publications

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“…This result is somewhat surprising given that the wet system contains more than three times as many channels (64 vs. 18), although earlier work has shown a channel reduction from 96 to 20 had limited effect on decoding accuracy [14]. Given these results, and recent studies that suggest that wet and dry EEG systems can deliver similar signal qualities (albeit with different systems than we used) [36], a practical integration of AAD into an unobtrusive, wearable hearing device seems to be an achievable, long-term goal.…”

Section: Discussionmentioning

confidence: 72%

Comparison of Two-Talker Attention Decoding from EEG with Nonlinear Neural Networks and Linear Methods

Ciccarelli

Nolan

Perricone

et al. 2018

Preprint

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Auditory attention decoding (AAD) through a brain-computer interface has had a flowering of developments since it was first introduced by Mesgarani and Chang (2012) using electrocorticograph recordings. AAD has been pursued for its potential application to hearing-aid design in which an attention-guided algorithm selects, from multiple competing acoustic sources, which should be enhanced for the listener and which should be suppressed. Traditionally, researchers have separated the AAD problem into two stages: reconstruction of a representation of the attended audio from neural signals, followed by determining the similarity between the candidate audio streams and the reconstruction. In this work, we compare the traditional two-stage approach with a novel neural-network architecture that subsumes the explicit similarity step. We compare this new architecture against linear and non-linear (neural-network) baselines using both wet and dry electroencephalogram (EEG) systems. Our results indicate that the wet and dry systems can deliver comparable results despite the latter having one third as many EEG channels as the former, and that the new architecture outperforms the baseline stimulus-reconstruction methods for both EEG modalities. The 14-subject, wet-electrode AAD dataset for two competing, co-located talkers, the 11-subject, dry-electrode AAD dataset, and our software are available to download for further validation, experimentation, and modification.

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Section: Discussionmentioning

confidence: 72%

Comparison of Two-Talker Attention Decoding from EEG with Nonlinear Neural Networks and Linear Methods

Ciccarelli

Nolan

Perricone

et al. 2018

Preprint

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“…Namely, both feasibility and acceptability of sleep EEG devices in this age group were negatively impacted by specific features of the device, such as the wires, which contributed to both lost data (by becoming unplugged) and some, usually mild, discomfort. Wireless EEG systems have been developed and tested [ 43 ], and if successfully adapted, may have utility for assessing sleep in adolescents. Relatedly, unlike other commercial-grade devices, the device was attached to the head only via sensors rather than with a headband or cap [ 24 ].…”

Section: Discussionmentioning

confidence: 99%

Feasibility and Acceptability of Wearable Sleep Electroencephalogram Device Use in Adolescents: Observational Study

Lunsford‐Avery¹,

Keller²,

Kollins³

et al. 2020

JMIR Mhealth Uhealth

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Background Adolescence is an important life stage for the development of healthy behaviors, which have a long-lasting impact on health across the lifespan. Sleep undergoes significant changes during adolescence and is linked to physical and psychiatric health; however, sleep is rarely assessed in routine health care settings. Wearable sleep electroencephalogram (EEG) devices may represent user-friendly methods for assessing sleep among adolescents, but no studies to date have examined the feasibility and acceptability of sleep EEG wearables in this age group. Objective The goal of the research was to investigate the feasibility and acceptability of sleep EEG wearable devices among adolescents aged 11 to 17 years. Methods A total of 104 adolescents aged 11 to 17 years participated in 7 days of at-home sleep recording using a self-administered wearable sleep EEG device (Zmachine Insight+, General Sleep Corporation) as well as a wristworn actigraph. Feasibility was assessed as the number of full nights of successful recording completed by adolescents, and acceptability was measured by the wearable acceptability survey for sleep. Feasibility and acceptability were assessed separately for the sleep EEG device and wristworn actigraph. Results A total of 94.2% (98/104) of adolescents successfully recorded at least 1 night of data using the sleep EEG device (mean number of nights 5.42; SD 1.71; median 6, mode 7). A total of 81.6% (84/103) rated the comfort of the device as falling in the comfortable to mildly uncomfortable range while awake. A total of 40.8% (42/103) reported typical sleep while using the device, while 39.8% (41/103) indicated minimal to mild device-related sleep disturbances. A minority (32/104, 30.8%) indicated changes in their sleep position due to device use, and very few (11/103, 10.7%) expressed dissatisfaction with their experience with the device. A similar pattern was observed for the wristworn actigraph device. Conclusions Wearable sleep EEG appears to represent a feasible, acceptable method for sleep assessment among adolescents and may have utility for assessing and treating sleep disturbances at a population level. Future studies with adolescents should evaluate strategies for further improving usability of such devices, assess relationships between sleep EEG–derived metrics and health outcomes, and investigate methods for incorporating data from these devices into emerging digital interventions and applications. Trial Registration ClinicalTrials.gov NCT03843762; https://clinicaltrials.gov/ct2/show/NCT03843762

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“…It has recently been demonstrated that commercial-grade EEG equipment compares to high-grade equipment in laboratory settings in terms of neural reliability as quantified by intersubject correlation [Poulsen et al, 2017]. Furthermore, a systematic comparison between a wireless dry EEG system and a conventional laboratory-based wet EEG system shows similar performance in terms of signal quality [Kam et al, 2019].…”

Section: Discussionmentioning

confidence: 99%

Single-Trial Decoding of Scalp EEG Under Natural Conditions

Tuckute

Hansen

Pedersen

et al. 2018

Preprint

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There is significant current interest in decoding mental states from electro-encephalography (EEG) recordings. EEG signals are subject-specific, sensitive to disturbances, and have a low signal-to-noise ratio, which has been mitigated by the use of laboratory-grade EEG acquisition equipment under highly controlled conditions. In the present study, we investigate single-trial decoding of natural, complex stimuli based on scalp EEG acquired with a portable, 32 dry-electrode sensor system in a typical office setting. We probe generalizability by a leave-one-subject-out cross-validation approach. We demonstrate that Support Vector Machine (SVM) classifiers trained on a relatively small set of de-noised (averaged) pseudo-trials perform on par with classifiers trained on a large set of noisy single-trial samples. For visualization of EEG signatures exploited by SVM classifiers, we propose a novel method for computing sensitivity maps of EEG-based SVM classifiers. Moreover, we apply the NPAIRS resampling framework for estimation of map uncertainty and show that effect sizes of sensitivity maps for classifiers trained on small samples of de-noised data and large samples of noisy data are similar. Finally, we demonstrate that the average pseudo-trial classifier can successfully predict the class of single trials from withheld subjects, which allows for fast classifier training, parameter optimization and unbiased performance evaluation in machine learning approaches for brain decoding.

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

Cited by 145 publications

References 40 publications

Comparison of Two-Talker Attention Decoding from EEG with Nonlinear Neural Networks and Linear Methods

Comparison of Two-Talker Attention Decoding from EEG with Nonlinear Neural Networks and Linear Methods

Feasibility and Acceptability of Wearable Sleep Electroencephalogram Device Use in Adolescents: Observational Study

Single-Trial Decoding of Scalp EEG Under Natural Conditions

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