A case study on auditory brain activations relative to musical experience by means of eeg complexity levels

In this pre-work, entropy estimation methods have been firstly applied to single trial auditory oscillations mediated by tonal (non-target) and atonal (target) chords in order to show functional superior performance of musicians in auditory tasks. For this purpose, both embedding and probabilistic as well as wavelet entropy methods are compared to each other with respect to classification performance criteria in tests where two groups (5 musicians and 5 non-musicians) are classified by using 10-fold cross validated Support Vector Machine Classifiers for both target and non-target records. An embedding entropy so called Permutation Entropy provided the highest classification accuracies (99.96% for target records, 99.29% for non-target records) with respect to specified features extracted from 22 electrode locations near to auditory cortex (P7, TP7, T7, FT7, F7, AF7, AF8, F8, FT8, T8, TP8, P8, P5, CP5, C5, FC5, F5, F6, FC6, C6, CP6, P6). In conclusion, musical training enhances the neural encoding of sound and this performance can be quantified in terms of entropy values. In detail, the degree of EEG complexity increases depending on the existing of musical experience in auditory tasks.