The impact of musical experience on neural sound encoding performance

Aydın, Serap; Güdücü, Çağdaş; Kutluk, Fırat; Öniz, Adile; Özgören, Murat

doi:10.1016/j.neulet.2018.11.034

Cited by 30 publications

(8 citation statements)

References 43 publications

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“…For our study, 89 articles 47–132 were extracted from the Google Scholar and PubMed databases using the keywords “neuroplasticity,” “brain changes,” “music,” and “sound.” Based on these keywords, we included all articles that were published in 2019, written in English, and peer‐reviewed. The sample included both quantitative and qualitative studies.…”

Section: Methodsmentioning

confidence: 99%

Quality standards and recommendations for research in music and neuroplasticity

Papatzikis

Agapaki

Selvan

et al. 2022

Annals of the New York Academy of Sciences

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Research on how music influences brain plasticity has gained momentum in recent years. Considering, however, the nonuniform methodological standards implemented, the findings end up being nonreplicable and less generalizable. To address the need for a standardized baseline of research quality, we gathered all the studies in the music and neuroplasticity field in 2019 and appraised their methodological rigor systematically and critically. The aim was to provide a preliminary and, at the minimum, acceptable quality threshold-and, ipso facto, suggested recommendations-whereupon further discussion and development may take place. Quality appraisal was performed on 89 articles by three independent raters, following a standardized scoring system. The raters' scoring was cross-referenced following an inter-rater reliability measure, and further studied by performing multiple ratings comparisons and matrix analyses. The results for methodological quality were at a quite good level (quantitative articles: mean = 0.737, SD = 0.084; qualitative articles: mean = 0.677, SD = 0.144), following a moderate but statistically significant level of agreement between the raters (W = 0.44, χ 2 = 117.249, p = 0.020). We conclude that the standards for implementation and reporting are of high quality; however, certain improvements are needed to reach the stringent levels presumed for such an influential interdisciplinary scientific field.

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Section: Methodsmentioning

confidence: 99%

Quality standards and recommendations for research in music and neuroplasticity

Papatzikis

Agapaki

Selvan

et al. 2022

Annals of the New York Academy of Sciences

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show abstract

“…To obtain the required EEG dataset, volunteer participants were selected from th Faculty of Engineering and Green Technology, Universiti Tunku Abdul Rehman. The cor tical response of musician and non-musician subjects is different [30]; hence, all the par ticipants selected for this study were non-musicians. After explaining all the experimenta procedures, all the participants submitted a written signed consent.…”

Section: Participantsmentioning

confidence: 99%

The Effect of Music Listening on EEG Functional Connectivity of Brain: A Short-Duration and Long-Duration Study

et al. 2022

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Music is considered a powerful brain stimulus, as listening to it can activate several brain networks. Music of different kinds and genres may have a different effect on the human brain. The goal of this study is to investigate the change in the brain’s functional connectivity (FC) when music is used as a stimulus. Secondly, the effect of listening to the subject’s favorite music is compared with listening to specifically formulated relaxing music with alpha binaural beats. Finally, the effect of the duration of music listening is studied. Subjects’ electroencephalographic (EEG) signals were captured as they listened to favorite and relaxing music. After preprocessing and artifact removal, the EEG recordings were decomposed into the delta, theta, alpha, and beta frequency bands, and the grand-averaged connectivity matrices were generated using Inter-Site Phase Clustering (ISPC) for each frequency band and each type of music. Furthermore, each lobe of the brain was analyzed separately to understand the effect of music on specific regions of the brain. EEG-FC among different channels was accessed by using graph theory and Network-based Statistics (NBS). To determine the significance of the changes in brain networks after listening to music, statistical analysis was conducted using Analysis of Variance (ANOVA) and t-test. The study of listening to music for a short duration verifies that either favorite or preferred music can affect the FC of the subject and induce a relaxation state. The short duration study also verifies a significant (ANOVA and t-test: p < 0.05) effectiveness of relaxing music over favorite music to induce relaxation and alertness in the subject. In the study of long duration, it is concluded that listening to relaxing music can increase functional connectivity and connections strength in the frontal lobe of the subject. A significant increase (ANOVA and t-test: p < 0.05) in FC in alpha and theta band and a significant decrease (ANOVA and t-test: p < 0.05) in FC in beta band in the frontal and parietal lobe of the brain verifies the hypothesis that the relaxing music can help the subject to achieve relaxation, activeness, and alertness.

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“…The most commonly used classifiers are SVM [ 23 , 35 , 38 ], k-nearest neighbors classifiers (kNN) [ 3 , 15 , 39 ], or neural networks [ 13 , 27 , 37 ]. Previous literature support that SVMs, kNN, and neural networks are, in fact, the most valuable methods for biomedical applications, such as ECG analysis [ 40 ] and the classification of ECG and EEG features for the detection of various disorders [ 41 , 42 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Initial Study Using Electrocardiogram for Authentication and Identification

Pereira

Conceição

Sebastião

2022

Sensors

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Recently, several studies have demonstrated the potential of electrocardiogram (ECG) to be used as a physiological signature for biometric systems (BS). We investigated the potential of ECG as a biometric trait for the identification and authentication of individuals. We used data from a public database, CYBHi, containing two off-the-person records from 63 subjects, separated by 3 months. For the BS, two templates were generated: (1) cardiac cycles (CC) and (2) scalograms. The identification with CC was performed with LDA, kNN, DT, and SVM, whereas a convolutional neural network (CNN) and a distance-based algorithm were used for scalograms. The authentication was performed with a distance-based algorithm, with a leave-one-out cross validation, for impostors evaluation. The identification system yielded accuracies of 79.37% and 69.84% for CC with LDA and scalograms with CNN, respectively. The authentication yielded an accuracy of 90.48% and an impostor score of 13.06% for CC, and it had an accuracy of 98.42% and an impostor score of 14.34% for scalograms. The obtained results support the claim that ECG can be successfully used for personal recognition. To the best of our knowledge, our study is the first to thoroughly compare templates and methodologies to optimize the performance of an ECG-based biometric system.

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The impact of musical experience on neural sound encoding performance

Cited by 30 publications

References 43 publications

Quality standards and recommendations for research in music and neuroplasticity

Quality standards and recommendations for research in music and neuroplasticity

The Effect of Music Listening on EEG Functional Connectivity of Brain: A Short-Duration and Long-Duration Study

Initial Study Using Electrocardiogram for Authentication and Identification

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