Emotion recognition of music objects is one of the promising research issues in the field of music information retrieval. Usually, music emotion recognition could be considered as a training/classification problem. However, even if we have a benchmark (a training data with ground truth) and employ effective classification algorithms, music emotion recognition remains a challenging problem. Based on our literature review, most of previous work only focuses on music acoustic content without considering the individual difference (i.e., Personalization issue). In addition, the assessment of emotions are usually self-reported. Such kind of self-reported assessment (e.g., Emotion tags) might be inaccurate, and even inconsistent. The electroencephalography (EEG) is a non-invasive brain-machine interface, which utilizes neurophysiological signals from the brain to external machines without surgery. The less-intrusive EEG signals, captured from the central nervous system, have been utilized for exploring emotions. In this paper, we would like to propose an evidence-based and personalized model for music emotion recognition. In the model construction and training phase, we construct two predictive and generic models (both models are trained by artificial neural network). With having the generic model and the corresponding individual difference, we construct the personalized model H by the projective transformation accordingly. In the testing phase, given a music object, we extract features from music audio content, calculate the vector in the arousal-valence emotion space, and apply the transformation matrix H to determine the personalized emotion vector. To show the effectiveness of our approach, we also perform experiments and obtain promising results.