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      Cortical Sensitivity to Guitar Note Patterns: EEG Entrainment to Repetition and Key

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          Abstract

          Music is ubiquitous throughout recent human culture, and many individual's have an innate ability to appreciate and understand music. Our appreciation of music likely emerges from the brain's ability to process a series of repeated complex acoustic patterns. In order to understand these processes further, cortical responses were measured to a series of guitar notes presented with a musical pattern or without a pattern. ERP responses to individual notes were measured using a 24 electrode Bluetooth mobile EEG system (Smarting mBrainTrain) while 13 healthy non-musicians listened to structured (i.e., within musical keys and with repetition) or random sequences of guitar notes for 10 min each. We demonstrate an increased amplitude to the ERP that appears ~200 ms to notes presented within the musical sequence. This amplitude difference between random notes and patterned notes likely reflects individual's cortical sensitivity to guitar note patterns. These amplitudes were compared to ERP responses to a rare note embedded within a stream of frequent notes to determine whether the sensitivity to complex musical structure overlaps with the sensitivity to simple irregularities reflected in traditional auditory oddball experiments. Response amplitudes to the negative peak at ~175 ms are statistically correlated with the mismatch negativity (MMN) response measured to a rare note presented among a series of frequent notes (i.e., in a traditional oddball sequence), but responses to the subsequent positive peak at ~200 do not show a statistical relationship with the P300 response. Thus, the sensitivity to musical structure identified to 4 Hz note patterns appears somewhat distinct from the sensitivity to statistical regularities reflected in the traditional “auditory oddball” sequence. Overall, we suggest that this is a promising approach to examine individual's sensitivity to complex acoustic patterns, which may overlap with higher level cognitive processes, including language.

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          Most cited references62

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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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            Independent component analysis using an extended infomax algorithm for mixed subgaussian and supergaussian sources.

            An extension of the infomax algorithm of Bell and Sejnowski (1995) is presented that is able blindly to separate mixed signals with sub- and supergaussian source distributions. This was achieved by using a simple type of learning rule first derived by Girolami (1997) by choosing negentropy as a projection pursuit index. Parameterized probability distributions that have sub- and supergaussian regimes were used to derive a general learning rule that preserves the simple architecture proposed by Bell and Sejnowski (1995), is optimized using the natural gradient by Amari (1998), and uses the stability analysis of Cardoso and Laheld (1996) to switch between sub- and supergaussian regimes. We demonstrate that the extended infomax algorithm is able to separate 20 sources with a variety of source distributions easily. Applied to high-dimensional data from electroencephalographic recordings, it is effective at separating artifacts such as eye blinks and line noise from weaker electrical signals that arise from sources in the brain.
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              FASTER: Fully Automated Statistical Thresholding for EEG artifact Rejection.

              Electroencephalogram (EEG) data are typically contaminated with artifacts (e.g., by eye movements). The effect of artifacts can be attenuated by deleting data with amplitudes over a certain value, for example. Independent component analysis (ICA) separates EEG data into neural activity and artifact; once identified, artifactual components can be deleted from the data. Often, artifact rejection algorithms require supervision (e.g., training using canonical artifacts). Many artifact rejection methods are time consuming when applied to high-density EEG data. We describe FASTER (Fully Automated Statistical Thresholding for EEG artifact Rejection). Parameters were estimated for various aspects of data (e.g., channel variance) in both the EEG time series and in the independent components of the EEG: outliers were detected and removed. FASTER was tested on both simulated EEG (n=47) and real EEG (n=47) data on 128-, 64-, and 32-scalp electrode arrays. FASTER was compared to supervised artifact detection by experts and to a variant of the Statistical Control for Dense Arrays of Sensors (SCADS) method. FASTER had >90% sensitivity and specificity for detection of contaminated channels, eye movement and EMG artifacts, linear trends and white noise. FASTER generally had >60% sensitivity and specificity for detection of contaminated epochs, vs. 0.15% for SCADS. FASTER also aggregates the ERP across subject datasets, and detects outlier datasets. The variance in the ERP baseline, a measure of noise, was significantly lower for FASTER than either the supervised or SCADS methods. ERP amplitude did not differ significantly between FASTER and the supervised approach. Copyright 2010 Elsevier B.V. All rights reserved.
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                Author and article information

                Contributors
                Journal
                Front Hum Neurosci
                Front Hum Neurosci
                Front. Hum. Neurosci.
                Frontiers in Human Neuroscience
                Frontiers Media S.A.
                1662-5161
                01 March 2017
                2017
                : 11
                : 90
                Affiliations
                [1] 1The Mind Research Network Albuquerque, NM, USA
                [2] 2Department of Electrical and Computer Engineering, University of New Mexico Albuquerque, NM, USA
                [3] 3The MARC Program, University of New Mexico Albuquerque, NM, USA
                Author notes

                Edited by: Klaus Gramann, Technical University of Berlin, Germany

                Reviewed by: Evangelos Paraskevopoulos, Aristotle University of Thessaloniki, Greece; Grace Leslie, University of California, San Diego, USA

                *Correspondence: David A. Bridwell dbridwell@ 123456mrn.org
                Article
                10.3389/fnhum.2017.00090
                5331856
                28298889
                56c5f2f7-a1a4-45a2-832d-003b92738cad
                Copyright © 2017 Bridwell, Leslie, McCoy, Plis and Calhoun.

                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) or licensor 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 September 2016
                : 14 February 2017
                Page count
                Figures: 3, Tables: 0, Equations: 0, References: 70, Pages: 9, Words: 7161
                Funding
                Funded by: National Institutes of Health 10.13039/100000002
                Award ID: R01EB005846
                Award ID: 5P20GM103472
                Funded by: National Science Foundation 10.13039/100000001
                Award ID: 1539067
                Award ID: IIS-1318759
                Categories
                Neuroscience
                Original Research

                Neurosciences
                mobile eeg,music,oddball,guitar,ssaep,frequency tagging
                Neurosciences
                mobile eeg, music, oddball, guitar, ssaep, frequency tagging

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