On the efficiency of long short-term memory in classifying musical impressions from EEG recordings

The objective of this study is the classification of musical impressions with long short-term memory (LSTM) approach using EEG recordings of 20 subjects, while listening to different music genres. For this purpose, a deep learning model was developed, where relevant features extracted from intrinsic mode functions (IMF) of the clean EEG data are used as the input signals. The classification accuracy of the proposed model is evaluated with various feature sets. The highest classification accuracy is 73.33%, which is achieved by combining higher-order statistics and the first difference of IMF features.