Multi-feature Comparison of Algorithmic Music’s Influence on EEG

In order to explore the difference between the brain’s response to low-quality music composed by algorithms and high-quality music composed by humans, Long short-term memory (LSTM) was used to generate algorithmic music with different audibility. The selected part of the algorithmic music was divided into 5 levels according to the comprehensive evaluation scores, and the popular music melody excerpts were regarded as the 6th level, used as the music material for paradigm experiments. Feature extraction methods including differential entropy, power spectral density, Hjorth parameters and other frequency domain analysis, were used to the feature extracting, the data analysing and the significance judgement of experimental electroencephalogram (EEG) signals, which were carried out to compare the difference of EEG signals when subjects were listening to music with different audibility. The differences of EEG characteristics of subjects with different gender and music professional background when listening to the same music were further analysed. The experimental results show that the power spectral density (PSD) is the best feature extraction method for comparing the difference of EEG signals.