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      HEROIC: a platform for remote collection of electroencephalographic data using consumer-grade brain wearables

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          Abstract

          The growing number of portable consumer-grade electroencephalography (EEG) wearables offers potential to track brain activity and neurological disease in real-world environments. However, accompanying open software tools to standardize custom recordings and help guide independent operation by users is lacking. To address this gap, we developed HEROIC, an open-source software that allows participants to remotely collect advanced EEG data without the aid of an expert technician. The aim of HEROIC is to provide an open software platform that can be coupled with consumer grade wearables to record EEG data during customized neurocognitive tasks outside of traditional research environments. This article contains a description of HEROIC’s implementation, how it can be used by researchers and a proof-of-concept demonstration highlighting the potential for HEROIC to be used as a scalable and low-cost EEG data collection tool. Specifically, we used HEROIC to guide healthy participants through standardized neurocognitive tasks and captured complex brain data including event-related potentials (ERPs) and powerband changes in participants’ homes. Our results demonstrate HEROIC’s capability to generate data precisely synchronized to presented stimuli, using a low-cost, remote protocol without reliance on an expert operator to administer sessions. Together, our software and its capabilities provide the first democratized and scalable platform for large-scale remote and longitudinal analysis of brain health and disease.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12859-024-05865-9.

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          EEG differences between eyes-closed and eyes-open resting conditions.

          Recent work has attempted to clarify the energetics of physiological responding and behaviour by refining and separating the operational definitions of "arousal" and "activation", which have different effects on physiological responding and behaviour. At the EEG level, we relate the former to widespread activity, and the latter to task-specific topographically-focussed activity reflecting regional processing. This study aimed to investigate this further in terms of differences in EEG activity between eyes-closed and eyes-open resting conditions. EEG activity was recorded from 28 university students during both eyes-closed and eyes-open resting conditions, Fourier transformed to provide estimates for absolute power in the delta, theta, alpha and beta bands, and analysed in 9 regions across the scalp. Skin conductance level was also measured as an indicator of arousal level. Across the eyes-closed conditions, skin conductance levels were negatively correlated with mean alpha levels. Skin conductance levels increased significantly from eyes-closed to eyes-open conditions. Reductions were found in across-scalp mean absolute delta, theta, alpha and beta from the eyes-closed to eyes-open condition. Topographic changes were also evident in all bands except for alpha, with reduced lateral frontal delta and posterior theta, and decreased posterior/increased frontal beta in the eyes-open condition. In particular, the topographic beta effects indicate that the across-scalp reduction arose from focal reductions rather than global changes. The obtained results confirm the use of mean alpha level as a measure of resting-state arousal under eyes-closed and eyes-open conditions. The focal nature of EEG effects in the other bands suggests that these reflect cortical processing of visual input, producing differences in activation between eyes-closed and eyes-open resting conditions, rather than just the simple increase in arousal level shown in alpha. This study demonstrates that the eyes-closed and eyes-open conditions provide EEG measures differing in topography as well as power levels. These differences should be recognised when evaluating EEG research, and considered when choosing eyes-open or eyes-closed baseline conditions for different paradigms.
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            Choosing MUSE: Validation of a Low-Cost, Portable EEG System for ERP Research

            In recent years there has been an increase in the number of portable low-cost electroencephalographic (EEG) systems available to researchers. However, to date the validation of the use of low-cost EEG systems has focused on continuous recording of EEG data and/or the replication of large system EEG setups reliant on event-markers to afford examination of event-related brain potentials (ERP). Here, we demonstrate that it is possible to conduct ERP research without being reliant on event markers using a portable MUSE EEG system and a single computer. Specifically, we report the results of two experiments using data collected with the MUSE EEG system—one using the well-known visual oddball paradigm and the other using a standard reward-learning task. Our results demonstrate that we could observe and quantify the N200 and P300 ERP components in the visual oddball task and the reward positivity (the mirror opposite component to the feedback-related negativity) in the reward-learning task. Specifically, single sample t-tests of component existence (all p's < 0.05), computation of Bayesian credible intervals, and 95% confidence intervals all statistically verified the existence of the N200, P300, and reward positivity in all analyses. We provide with this research paper an open source website with all the instructions, methods, and software to replicate our findings and to provide researchers with an easy way to use the MUSE EEG system for ERP research. Importantly, our work highlights that with a single computer and a portable EEG system such as the MUSE one can conduct ERP research with ease thus greatly extending the possible use of the ERP methodology to a variety of novel contexts.
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              Wearable EEG and beyond

              The electroencephalogram (EEG) is a widely used non-invasive method for monitoring the brain. It is based upon placing conductive electrodes on the scalp which measure the small electrical potentials that arise outside of the head due to neuronal action within the brain. Historically this has been a large and bulky technology, restricted to the monitoring of subjects in a lab or clinic while they are stationary. Over the last decade much research effort has been put into the creation of “wearable EEG” which overcomes these limitations and allows the long term non-invasive recording of brain signals while people are out of the lab and moving about. This paper reviews the recent progress in this field, with particular emphasis on the electrodes used to make connections to the head and the physical EEG hardware. The emergence of conformal “tattoo” type EEG electrodes is highlighted as a key next step for giving very small and socially discrete units. In addition, new recommendations for the performance validation of novel electrode technologies are given, with standards in this area seen as the current main bottleneck to the wider take up of wearable EEG. The paper concludes by considering the next steps in the creation of next generation wearable EEG units, showing that a wide range of research avenues are present.
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                Author and article information

                Contributors
                p.diamandis@mail.utoronto.ca
                Journal
                BMC Bioinformatics
                BMC Bioinformatics
                BMC Bioinformatics
                BioMed Central (London )
                1471-2105
                18 July 2024
                18 July 2024
                2024
                : 25
                : 243
                Affiliations
                [1 ]Department of Medical Biophysics, University of Toronto, ( https://ror.org/03dbr7087) Toronto, ON M5S 1A8 Canada
                [2 ]GRID grid.231844.8, ISNI 0000 0004 0474 0428, Princess Margaret Cancer Center, , University Health Network, ; 610 University Avenue, Toronto, ON M5G 2C1 Canada
                [3 ]Department of Laboratory Medicine and Pathobiology, University of Toronto, ( https://ror.org/03dbr7087) Toronto, ON M5S 1A8 Canada
                [4 ]Laboratory Medicine Program, University Health Network, ( https://ror.org/042xt5161) 200 Elizabeth Street, Toronto, ON M5G 2C4 Canada
                [5 ]Cognitive Science Program, McGill University, ( https://ror.org/01pxwe438) 845 Rue Sherbrooke O, Montréal, QC H3A 0G4 Canada
                [6 ]Department of Supportive Care, Princess Margaret Cancer Centre, ( https://ror.org/03zayce58) Toronto, ON M5G 2C4 Canada
                [7 ]Department of Psychiatry, University of Toronto, ( https://ror.org/03dbr7087) Toronto, ON M5S 1A8 Canada
                [8 ]GRID grid.231844.8, ISNI 0000 0004 0474 0428, Department of Pathology, University Health Network 12-308, , Toronto Medical Discovery Tower (TMDT), ; 101 College St, Toronto, M5G 1L7 Canada
                Article
                5865
                10.1186/s12859-024-05865-9
                11256487
                39026153
                7c0369ab-f312-4172-a49b-27eb265b535a
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 23 May 2024
                : 10 July 2024
                Funding
                Funded by: Adam Coules Brain Tumor Research Grant
                Funded by: American Brain Tumor Association,United States
                Award ID: #1051131
                Award Recipient :
                Categories
                Software
                Custom metadata
                © BioMed Central Ltd., part of Springer Nature 2024

                Bioinformatics & Computational biology
                electroencephalography,wearable devices,remote medicine

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