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      Experience Affects EEG Event-Related Synchronization in Dancers and Non-dancers While Listening to Preferred Music

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          Abstract

          EEGs were analyzed to investigate the effect of experiences in listening to preferred music in dancers and non-dancers. Participants passively listened to instrumental music of their preferred genre for 2 min (Argentine tango for dancers, classical, or jazz for non-dancers), alternate genres, and silence. Both groups showed increased activity for their preferred music compared to non-preferred music in the gamma, beta, and alpha frequency bands. The results suggest all participants' conscious recognition of and affective responses to their familiar music (gamma), appreciation of the tempo embedded in their preferred music and emotional arousal (beta), and enhanced attention mechanism for cognitive operations such as memory retrieval (alpha). The observed alpha activity is considered in the framework of the alpha functional inhibition hypothesis, in that years of experience listening to their favorite type of music may have honed the cerebral responses to achieve efficient cortical processes. Analyses of the electroencephalogram (EEG) activity over 100s-long music pieces revealed a difference between dancers and non-dancers in the magnitude of an initial alpha event-related desynchronization (ERD) and the later development of an alpha event-related synchronization (ERS) for their preferred music. Dancers exhibited augmented alpha ERD, as well as augmented and uninterrupted alpha ERS over the remaining 80s. This augmentation in dancers is hypothesized to be derived from creative cognition or motor imagery operations developed through their dance experiences.

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          Most cited references51

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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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            Alpha-band oscillations, attention, and controlled access to stored information

            Alpha-band oscillations are the dominant oscillations in the human brain and recent evidence suggests that they have an inhibitory function. Nonetheless, there is little doubt that alpha-band oscillations also play an active role in information processing. In this article, I suggest that alpha-band oscillations have two roles (inhibition and timing) that are closely linked to two fundamental functions of attention (suppression and selection), which enable controlled knowledge access and semantic orientation (the ability to be consciously oriented in time, space, and context). As such, alpha-band oscillations reflect one of the most basic cognitive processes and can also be shown to play a key role in the coalescence of brain activity in different frequencies.
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              Removing electroencephalographic artifacts by blind source separation.

              Eye movements, eye blinks, cardiac signals, muscle noise, and line noise present serious problems for electroencephalographic (EEG) interpretation and analysis when rejecting contaminated EEG segments results in an unacceptable data loss. Many methods have been proposed to remove artifacts from EEG recordings, especially those arising from eye movements and blinks. Often regression in the time or frequency domain is performed on parallel EEG and electrooculographic (EOG) recordings to derive parameters characterizing the appearance and spread of EOG artifacts in the EEG channels. Because EEG and ocular activity mix bidirectionally, regressing out eye artifacts inevitably involves subtracting relevant EEG signals from each record as well. Regression methods become even more problematic when a good regressing channel is not available for each artifact source, as in the case of muscle artifacts. Use of principal component analysis (PCA) has been proposed to remove eye artifacts from multichannel EEG. However, PCA cannot completely separate eye artifacts from brain signals, especially when they have comparable amplitudes. Here, we propose a new and generally applicable method for removing a wide variety of artifacts from EEG records based on blind source separation by independent component analysis (ICA). Our results on EEG data collected from normal and autistic subjects show that ICA can effectively detect, separate, and remove contamination from a wide variety of artifactual sources in EEG records with results comparing favorably with those obtained using regression and PCA methods. ICA can also be used to analyze blink-related brain activity.
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                Author and article information

                Contributors
                Journal
                Front Psychol
                Front Psychol
                Front. Psychol.
                Frontiers in Psychology
                Frontiers Media S.A.
                1664-1078
                12 April 2021
                2021
                : 12
                : 611355
                Affiliations
                [1] 1Department of Psychology, Saint Mary's College of California , Moraga, CA, United States
                [2] 2Department of Physics and Astronomy, Saint Mary's College of California , Moraga, CA, United States
                [3] 3Center for Neurobehavioral Research on Addiction, Department of Psychiatry and Behavioral Sciences, University of Texas Health Science Center , Houston, TX, United States
                Author notes

                Edited by: Virginia Penhune, Concordia University, Canada

                Reviewed by: Raksha Anand Mudar, University of Illinois at Urbana-Champaign, United States; Marco Ivaldi, University of Turin, Italy

                *Correspondence: Hiroko Nakano hn1@ 123456stmarys-ca.edu

                This article was submitted to Auditory Cognitive Neuroscience, a section of the journal Frontiers in Psychology

                Article
                10.3389/fpsyg.2021.611355
                8071982
                37102254-7686-42aa-abdd-51aacc0773cd
                Copyright © 2021 Nakano, Rosario and de Dios.

                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
                : 29 September 2020
                : 10 March 2021
                Page count
                Figures: 3, Tables: 2, Equations: 1, References: 51, Pages: 13, Words: 9671
                Categories
                Psychology
                Original Research

                Clinical Psychology & Psychiatry
                music,expert,dance,eeg,alpha,beta,gamma,time frequency analysis (tfa)
                Clinical Psychology & Psychiatry
                music, expert, dance, eeg, alpha, beta, gamma, time frequency analysis (tfa)

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