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      Brain Synchrony in Competition and Collaboration During Multiuser Neurofeedback-Based Gaming

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          Abstract

          EEG hyperscanning during multiuser gaming offers opportunities to study brain characteristics of social interaction under various paradigms. In this study, we aimed to characterize neural signatures and phase-based functional connectivity patterns of gaming strategies during collaborative and competitive alpha neurofeedback games. Twenty pairs of participants with no close relationship took part in three sessions of collaborative or competitive multiuser neurofeedback (NF), with identical graphical user interface, using Relative Alpha (RA) power as a control signal. Collaborating dyads had to keep their RA within 5% of each other for the team to be awarded a point, while members of competitive dyads scored points if their RA was 10% above their opponent's. Interbrain synchrony existed only during gaming but not during baseline in either collaborative or competitive gaming. Spectral analysis and interbrain connectivity showed that in collaborative gaming, players with higher resting state alpha content were more active in regulating their RA to match those of their partner. Moreover, interconnectivity was the strongest between homologous brain structures of the dyad in theta and alpha bands, indicating a similar degree of planning and social exchange. Competitive gaming emphasized the difference between participants who were able to relax and, in this way, maintain RA, and those who had an unsuccessful approach. Analysis of interbrain connections shows engagement of frontal areas in losers, but not in winners, indicating the formers' attempt to mentalise and apply strategies that might be suitable for conventional gaming, but inappropriate for the alpha neurofeedback-based game. We show that in gaming based on multiplayer non-verbalized NF, the winning strategy is dependent on the rules of the game and on the behavior of the opponent. Mental strategies that characterize successful gaming in the physical world might not be adequate for NF-based gaming.

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis

            We have developed a toolbox and graphic user interface, EEGLAB, running under the crossplatform MATLAB environment (The Mathworks, Inc.) for processing collections of single-trial and/or averaged EEG data of any number of channels. Available functions include EEG data, channel and event information importing, data visualization (scrolling, scalp map and dipole model plotting, plus multi-trial ERP-image plots), preprocessing (including artifact rejection, filtering, epoch selection, and averaging), independent component analysis (ICA) and time/frequency decompositions including channel and component cross-coherence supported by bootstrap statistical methods based on data resampling. EEGLAB functions are organized into three layers. Top-layer functions allow users to interact with the data through the graphic interface without needing to use MATLAB syntax. Menu options allow users to tune the behavior of EEGLAB to available memory. Middle-layer functions allow users to customize data processing using command history and interactive 'pop' functions. Experienced MATLAB users can use EEGLAB data structures and stand-alone signal processing functions to write custom and/or batch analysis scripts. Extensive function help and tutorial information are included. A 'plug-in' facility allows easy incorporation of new EEG modules into the main menu. EEGLAB is freely available (http://www.sccn.ucsd.edu/eeglab/) under the GNU public license for noncommercial use and open source development, together with sample data, user tutorial and extensive documentation.
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              A default mode of brain function.

              A baseline or control state is fundamental to the understanding of most complex systems. Defining a baseline state in the human brain, arguably our most complex system, poses a particular challenge. Many suspect that left unconstrained, its activity will vary unpredictably. Despite this prediction we identify a baseline state of the normal adult human brain in terms of the brain oxygen extraction fraction or OEF. The OEF is defined as the ratio of oxygen used by the brain to oxygen delivered by flowing blood and is remarkably uniform in the awake but resting state (e.g., lying quietly with eyes closed). Local deviations in the OEF represent the physiological basis of signals of changes in neuronal activity obtained with functional MRI during a wide variety of human behaviors. We used quantitative metabolic and circulatory measurements from positron-emission tomography to obtain the OEF regionally throughout the brain. Areas of activation were conspicuous by their absence. All significant deviations from the mean hemisphere OEF were increases, signifying deactivations, and resided almost exclusively in the visual system. Defining the baseline state of an area in this manner attaches meaning to a group of areas that consistently exhibit decreases from this baseline, during a wide variety of goal-directed behaviors monitored with positron-emission tomography and functional MRI. These decreases suggest the existence of an organized, baseline default mode of brain function that is suspended during specific goal-directed behaviors.
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                Author and article information

                Contributors
                Journal
                Front Neurogenom
                Front Neurogenom
                Front. Neuroergon.
                Frontiers in Neuroergonomics
                Frontiers Media S.A.
                2673-6195
                01 November 2021
                2021
                : 2
                : 749009
                Affiliations
                [1] 1Biomedical Research Division, School of Engineering, University of Glasgow , Glasgow, United Kingdom
                [2] 2Section of Movement Disorders and Neurostimulation, Biomedical Statistics and Multimodal Signal Processing Unit, Department of Neurology, Focus Program Translational Neuroscience (FTN), University Medical Center of the Johannes Gutenberg-University Mainz , Mainz, Germany
                Author notes

                Edited by: Domen Novak, University of Cincinnati, United States

                Reviewed by: Ali Darzi, University of Pittsburgh, United States; Marius Klug, Technical University of Berlin, Germany

                *Correspondence: Aleksandra Vuckovič aleksandra.vuckovic@ 123456glasgow.ac.uk

                This article was submitted to Neurotechnology and Systems Neuroergonomics, a section of the journal Frontiers in Neuroergonomics

                Article
                10.3389/fnrgo.2021.749009
                10790838
                38235241
                8c8ec99c-7f80-4688-a9c2-f617ea3acde1
                Copyright © 2021 Susnoschi Luca, Putri, Ding and Vuckovič.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 28 July 2021
                : 04 October 2021
                Page count
                Figures: 12, Tables: 0, Equations: 4, References: 71, Pages: 18, Words: 11275
                Funding
                Funded by: Engineering and Physical Sciences Research Council, doi 10.13039/501100000266;
                Award ID: EP/R513222/1ENG
                Categories
                Neuroergonomics
                Original Research

                bci,hyperscanning,functional connectivity,phase locking value (plv),eeg,neurofeedback,gaming

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