  A controversial issue in artificial intelligence is human emotion
recognition. This paper presents a fuzzy parallel cascades (FPC) model for
predicting the continuous subjective appraisal of the emotional content of
music by time-varying spectral content of EEG signals. The EEG, along with an
emotional appraisal of 15 subjects, was recorded during listening to seven
musical excerpts. The emotional appraisement was recorded along the valence and
arousal emotional axes as a continuous signal. The FPC model was composed of
parallel cascades with each cascade containing a fuzzy logic-based system. The
FPC model performance was evaluated by comparing with linear regression (LR),
support vector regression (SVR) and Long Short Term Memory recurrent neural
network (LSTM RNN) models. The RMSE of the FPC was lower than other models for
the estimation of both valence and arousal of all musical excerpts. The lowest
RMSE was 0.089 which was obtained in estimation of the valence of MS4 by the
FPC model. The analysis of MI of frontal EEG with the valence confirms the role
of frontal channels in theta frequency band in emotion recognition. Considering
the dynamic variations of musical features during songs, employing a modeling
approach to predict dynamic variations of the emotional appraisal can be a
plausible substitute for the classification of musical excerpts into predefined
labels.
