Affective content analysis of music emotion through EEG

Emotion recognition of music objects is a promising and important research issues in the field of music information retrieval. Usually, music emotion recognition could be considered as a training/classification problem. However, even given a benchmark (a training data with ground truth) and using effective classification algorithms, music emotion recognition remains a challenging problem. Most previous relevant work focuses only on acoustic music content without considering individual difference (i.e., personalization issues). In addition, assessment of emotions is usually self-reported (e.g., emotion tags) which might introduce inaccuracy and inconsistency. Electroencephalography (EEG) is a non-invasive brain-machine interface which allows external machines to sense neurophysiological signals from the brain without surgery. Such unintrusive EEG signals, captured from the central nervous system, have been utilized for exploring emotions. This paper proposes an evidence-based and personalized model for music emotion recognition. In the training phase for model construction and personalized adaption, based on the IADS (the International Affective Digitized Sound system, a set of acoustic emotional stimuli for experimental investigations of emotion and attention), we construct two predictive and generic models \(AN\!N_1\) (“EEG recordings of standardized group vs. emotions”) and \(AN\!N_2\) (“music audio content vs. emotion”). Both models are trained by an artificial neural network. We then collect a subject’s EEG recordings when listening the selected IADS samples, and apply the \(AN\!N_1\) to determine the subject’s emotion vector. With the generic model and the corresponding individual differences, we construct the personalized model H by the projective transformation. In the testing phase, given a music object, the processing steps are: (1) to extract features from the music audio content, (2) to apply \(AN\!N_2\) to calculate the vector in the arousal-valence emotion space, and (3) to apply the transformation matrix H to determine the personalized emotion vector. Moreover, with respect to a moderate music object, we apply a sliding window on the music object to obtain a sequence of personalized emotion vectors, in which those predicted vectors will be fitted and organized as an emotion trail for revealing dynamics in the affective content of music object. Experimental results suggest the proposed approach is effective.