Research Information System
NEUROSCIENCE LETTERS, vol.694, pp.124-128, 2019 (SCI-Expanded)
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.