The impact of musical experience on neural sound encoding performance


Aydin S., Guducu C., Kutluk F., Oniz A., Ozgoren M.

NEUROSCIENCE LETTERS, cilt.694, ss.124-128, 2019 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 694
  • Basım Tarihi: 2019
  • Doi Numarası: 10.1016/j.neulet.2018.11.034
  • Dergi Adı: NEUROSCIENCE LETTERS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED)
  • Sayfa Sayıları: ss.124-128
  • Anahtar Kelimeler: Brain, Music, Entropy, Auditory, Tonality, LEMPEL-ZIV COMPLEXITY, BANDWIDTH SELECTION, EEG, BRAIN, MODEL, MUSICIANS, SIGNALS
  • Hacettepe Üniversitesi Adresli: Evet

Özet

https://www.sciencedirect.com/science/article/pii/S0304394018308036?via%3Dihub

https://pubmed.ncbi.nlm.nih.gov/30503922/

ABSTRACT:

In this study, 64-channel single trial auditory brain oscillations (STABO) have been firstly analyzed by using complexity metrics to observe the effect of musical experience on brain functions. Experimental data was recorded from eyes-opened volunteers during listening the musical chords by piano. Complexity estimation methods were compared to each other for classification of groups (professional musicians and non-musicians) by using both classifiers (support vector machine (SVM), Naive Bayes (NB)) and statistical tests (one-way ANOVA) with respect to electrode locations. Permutation entropy (PermEn) is found to be the best metric (p << 0.0001, 98.37% and 98.41% accuracies for tonal and atonal ensembles) at fronto-temporal regions which are responsible for cognitive task evaluation and perception of sound. PermEn also provides the meaningful results at the whole cortex (p << 0.0001, 99.81% accuracy for both tonal and atonal ensembles) through SVM with Radial Basis kernels superior to Gaussians. Almost the similar performance is also obtained for temporal features. Although, performance improvements are observed for spectral methods with NB, the considerable better results are obtained with SVM. The results indicate that musical stimuli cause pattern variations instead of spectral variations on STABO due to relatively higher neuronal activities around auditory cortex. In conclusion, temporal regions produce response to auditory stimuli, while frontal area integrates the auditory task at the same time. As well, the parietal cortex produces neural information according to the degree of attention generated by environmental changes such as atonal stimuli. It can be clearly stated that musical experience enhances the neural encoding performance of sound tonality at mostly fronto-temporal regions.

CONCLUSION:

The results support that PMs provide the higher complexity levels due to the more neural activations at right fronto-temporal lobes. In conclusion, this finding can be explained by increasing processing capabilities in PMs. In other words, both experience and knowledge about the acoustic stimuli cause the high neuronal integration within neural ensembles together with at auditory cortex.

Tonal and atonal musical forms require discussion in regard to pleasure, primed memory, experience, fitness for music scales. Therefore, we can state the electrophysiological followings: Music processing is lateralized at mostly right hemisphere. A complex neural processing network is required in high cognitive musical functions such as musical attention, and the tracking of harmonic structure in time. Our current findings are compatible with the previous biological findings indicating that certain neurotransmitters (dopamine) is produced as a result of functions in amygdala affected by listening pleasure music [52]. The reference states that successfully tracking of tonal chords activates mostly prefrontal regions numbered by 44, 45, and 47 in Brodmann areas in addition to both anterior and posterior cingulate gyrus [53]. As well, musicians are capable of neural encoding of sounds due to excellent long-term musical training [54][55].

PermEn is found to be best complexity method to observe superior cognitive functions of musicians due to the fact that auditory stimuli induce amplitude variations in single trials. PermEn was proposed as useful method for estimation of neural dysfunctions such as epilepsy [56], seizure [57] and OCD [58]. As well, the computational complexity of PermEn is lower. In this study, PermEn provided the highest and successful results in every type of classification with 10-fold cross-validation. In nonparametric statistical problems, cross-validation methodologies have been used as a means of selecting tuning parameters. In future work, the method, proposed to improve the reliability of cross-validation [59], will be implemented in classification steps.

SVM classifiers have been frequently used to classify power spectra of EEG data including mental task [60] and seizure events [61] when the training set is large, since the optimal decision function can be obtained through statistical learning theory. However, kernel function specification affect the classification performance. In this study, RBF was found to be more useful for entropy estimations, even if the Gaussian kernels provide the relative improvements in classification performance for parametric and spectral estimations. However, NB classifiers provided the relatively much more improvements for the same parametric and spectral estimations in comparison to SVM with Gaussian kernels. In reference, RBF network shows better generalization performance and computationally faster than SVM with Gaussian kernel, specially for large training data sets [62]. In conclusion, both distribution of feature values and kernel types can be considered as performance parameters in EEG applications. The reference discuss the close relation between kernel function and classification accuracy for continuous time stationary process [63]. Feature set characteristics highly affect the performance of NB classifier.