Akademik Veri Yönetim Sistemi
NEUROSCIENCE LETTERS, cilt.694, ss.124-128, 2019 (SCI-Expanded)
https://pubmed.ncbi.nlm.nih.gov/30503922/
https://www.sciencedirect.com/science/article/pii/S0304394018308036?via%3Dihub
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.