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      Regulation of brain cognitive states through auditory, gustatory, and olfactory stimulation with wearable monitoring

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          Abstract

          Inspired by advances in wearable technologies, we design and perform human-subject experiments. We aim to investigate the effects of applying safe actuation (i.e., auditory, gustatory, and olfactory) for the purpose of regulating cognitive arousal and enhancing the performance states. In two proposed experiments, subjects are asked to perform a working memory experiment called n-back tasks. Next, we incorporate listening to different types of music, drinking coffee, and smelling perfume as safe actuators. We employ signal processing methods to seamlessly infer participants’ brain cognitive states. The results demonstrate the effectiveness of the proposed safe actuation in regulating the arousal state and enhancing performance levels. Employing only wearable devices for human monitoring and using safe actuation intervention are the key components of the proposed experiments. Our dataset fills the existing gap of the lack of publicly available datasets for the self-management of internal brain states using wearable devices and safe everyday actuators. This dataset enables further machine learning and system identification investigations to facilitate future smart work environments. This would lead us to the ultimate idea of developing practical automated personalized closed-loop architectures for managing internal brain states and enhancing the quality of life.

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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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            Dorsal and Ventral Attention Systems

            The idea of two separate attention networks in the human brain for the voluntary deployment of attention and the reorientation to unexpected events, respectively, has inspired an enormous amount of research over the past years. In this review, we will reconcile these theoretical ideas on the dorsal and ventral attentional system with recent empirical findings from human neuroimaging experiments and studies in stroke patients. We will highlight how novel methods—such as the analysis of effective connectivity or the combination of neurostimulation with functional magnetic resonance imaging—have contributed to our understanding of the functionality and interaction of the two systems. We conclude that neither of the two networks controls attentional processes in isolation and that the flexible interaction between both systems enables the dynamic control of attention in relation to top-down goals and bottom-up sensory stimulation. We discuss which brain regions potentially govern this interaction according to current task demands.
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              The PREP pipeline: standardized preprocessing for large-scale EEG analysis

              The technology to collect brain imaging and physiological measures has become portable and ubiquitous, opening the possibility of large-scale analysis of real-world human imaging. By its nature, such data is large and complex, making automated processing essential. This paper shows how lack of attention to the very early stages of an EEG preprocessing pipeline can reduce the signal-to-noise ratio and introduce unwanted artifacts into the data, particularly for computations done in single precision. We demonstrate that ordinary average referencing improves the signal-to-noise ratio, but that noisy channels can contaminate the results. We also show that identification of noisy channels depends on the reference and examine the complex interaction of filtering, noisy channel identification, and referencing. We introduce a multi-stage robust referencing scheme to deal with the noisy channel-reference interaction. We propose a standardized early-stage EEG processing pipeline (PREP) and discuss the application of the pipeline to more than 600 EEG datasets. The pipeline includes an automatically generated report for each dataset processed. Users can download the PREP pipeline as a freely available MATLAB library from http://eegstudy.org/prepcode.
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                Author and article information

                Contributors
                rfaghih@nyu.edu
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                8 August 2023
                8 August 2023
                2023
                : 13
                : 12399
                Affiliations
                [1 ]GRID grid.266436.3, ISNI 0000 0004 1569 9707, Electrical and Computer Engineering Department, , University of Houston, ; Houston, TX 77004 USA
                [2 ]GRID grid.266102.1, ISNI 0000 0001 2297 6811, Department of Neurological Surgery, , University of California San Francisco, ; San Francisco, CA 94143 USA
                [3 ]GRID grid.266436.3, ISNI 0000 0004 1569 9707, Biomedical Engineering Department, , University of Houston, ; Houston, TX 77004 USA
                [4 ]GRID grid.137628.9, ISNI 0000 0004 1936 8753, Department of Biomedical Engineering, , New York University, New York, ; New York, 10003 USA
                Article
                37829
                10.1038/s41598-023-37829-z
                10409795
                37553409
                edd945e8-149e-49af-bf88-796900a313ff
                © The Author(s) 2023

                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/.

                History
                : 3 February 2023
                : 28 June 2023
                Funding
                Funded by: National Science Foundation
                Award ID: 1755780
                Award Recipient :
                Categories
                Article
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                © Springer Nature Limited 2023

                Uncategorized
                neuroscience,psychology,biomarkers,engineering,mathematics and computing
                Uncategorized
                neuroscience, psychology, biomarkers, engineering, mathematics and computing

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