Assessing the Quality of Wearable EEG Systems Using Functional Connectivity

Introduction:The pathophysiology of pediatric musculoskeletal (MSK) pain is unclear, contributing to persistent challenges to its management.Objectives: This study hypothesizes that children and adolescents with chronic MSK pain (CPs) will show differences in electroencephalography (EEG) features at rest and during thermal pain modalities when compared with age-matched controls. Methods: One hundred forty-two CP patients and 45 age-matched healthy controls (HCs) underwent a standardized thermal tonic heat and cold stimulations, while a 21-electrode headset collected EEG data. Cohorts were compared with respect to their EEG features of spectral power, peak frequency, permutation entropy, weight phase-lag index, directed phase-lag index, and node degree at 4 frequency bands, namely, delta (1-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), and beta (13-30 Hz), at rest and during the thermal conditions. Results: At rest, CPs showed increased global delta (P 5 0.0493) and beta (P 5 0.0002) power in comparison with HCs. These findings provide further impetus for the investigation and prevention of long-lasting developmental sequalae of early life chronic pain processes. Although no cohort differences in pain intensity scores were found during the thermal pain modalities, CPs and HCs showed significant difference in changes in EEG spectral power, peak frequency, permutation entropy, and network functional connectivity at specific frequency bands (P , 0.05) during the tonic heat and cold stimulations. Conclusion: This suggests that EEG can characterize subtle differences in heat and cold pain sensitivity in CPs. The complementation of EEG and evoked pain in the clinical assessment of pediatric chronic MSK pain can better detect underlying pain mechanisms and changes in pain sensitivity.

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“…Then, EEG segments were exported in a custom MATLAB plug-in EEGapp (EEGapp, BIAPT lab, McGill University) for analysis. 46 …”

Section: Methodsmentioning

confidence: 99%

Electroencephalographic characteristics of children and adolescents with chronic musculoskeletal pain

Ocay

Teel

Luo

et al. 2022

PR9

Self Cite

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“…However, as might be expected, consumer-grade devices display considerably poorer signal-to-noise ratios than their research-grade counterparts. Mahdid et al [139] found that the functional connectivity of both Emotiv and OpenBCI systems compared poorly to researchgrade systems; Raduntz [140] found that the signal reliability of Emotiv was poor if the device did not exactly fit the participant's head; and Ekandem et al [141] observed that the signal quality of an Emotiv (specifically, the EPOC+) declines over time as it uses a built-in rechargeable battery. There were few validation studies for the remaining devices.…”

Section: Validationmentioning

confidence: 99%

A scoping review on the use of consumer-grade EEG devices for research

Sabio

Williams

McArthur

et al. 2022

Preprint

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BACKGROUND: Commercial electroencephalography (EEG) devices have become increasingly available over the last decade. These devices have been used in a wide variety of fields ranging from engineering to cognitive neuroscience. PURPOSE: The aim of this study was to chart peer-review articles that used currently available consumer-grade EEG devices to collect neural data. We provide an overview of the research conducted with these relatively more affordable and user-friendly devices. We also inform future research by exploring the current and potential scope of consumer-grade EEG. METHODS: We followed a five-stage methodological framework for a scoping review that included a systematic search using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. We searched the following electronic databases: PsycINFO, MEDLINE, Embase, Web of Science, and IEEE Xplore. We charted study data according to application (BCI, experimental research, validation, signal processing, and clinical) and location of use as indexed by the first author's country. RESULTS: We identified 916 studies that used data recorded with consumer-grade EEG: 531 were reported in journal articles and 385 in conference papers. Emotiv devices were most used, followed by the NeuroSky MindWave, OpenBCI, interaXon Muse, and MyndPlay Mindband. The most common use was for brain-computer interfaces, followed by experimental research, signal processing, validation, and clinical purposes. CONCLUSIONS: Consumer-grade EEG has proven to be a useful tool for neuroscientific research and will likely continue to be used well into the future. Our study provides a comprehensive review of their application, as well as future directions for researchers who wish to use these devices.

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“…Methods can rely on human intervention [27] or be (semi)automated. Representative examples of the latter include methods that rely on data statistics (e.g., [28,29]), spectral/connectivity profiles (e.g., [30][31][32]), blind source separation (e.g., [33,34]), adaptive filtering (e.g., [35,36]), and, more recently, on machine and deep learning approaches (e.g., [37][38][39][40]). Combinations of multiple such approaches have also been proposed (e.g., [41,42]).…”

Section: Introductionmentioning

confidence: 99%

Modulation Spectral Signal Representation for Quality Measurement and Enhancement of Wearable Device Data: A Technical Note

Tiwari

Cassani

Kshirsagar

et al. 2022

Sensors

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Wearable devices are burgeoning, and applications across numerous verticals are emerging, including human performance monitoring, at-home patient monitoring, and health tracking, to name a few. Off-the-shelf wearables have been developed with focus on portability, usability, and low-cost. As such, when deployed in highly ecological settings, wearable data can be corrupted by artifacts and by missing data, thus severely hampering performance. In this technical note, we overview a signal processing representation called the modulation spectrum. The representation quantifies the rate-of-change of different spectral magnitude components and is shown to separate signal from noise, thus allowing for improved quality measurement, quality enhancement, and noise-robust feature extraction, as well as for disease characterization. We provide an overview of numerous applications developed by the authors over the last decade spanning different wearable modalities and list the results obtained from experimental results alongside comparisons with various state-of-the-art benchmark methods. Open-source software is showcased with the hope that new applications can be developed. We conclude with a discussion on possible future research directions, such as context awareness, signal compression, and improved input representations for deep learning algorithms.

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Assessing the Quality of Wearable EEG Systems Using Functional Connectivity

Cited by 11 publications

References 48 publications

Electroencephalographic characteristics of children and adolescents with chronic musculoskeletal pain

Electroencephalographic characteristics of children and adolescents with chronic musculoskeletal pain

A scoping review on the use of consumer-grade EEG devices for research

Modulation Spectral Signal Representation for Quality Measurement and Enhancement of Wearable Device Data: A Technical Note

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