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Music can effectively improve people's emotions, and has now become an effective auxiliary treatment method in modern medicine. With the rapid development of neuroimaging, the relationship between music and brain function has attracted much attention. In this study, we proposed an integrated framework of multi-modal electroencephalogram (EEG) and functional near infrared spectroscopy (fNIRS) from data collection to data analysis to explore the effects of music (especially personal preferred music) on brain activity. During the experiment, each subject was listening to two different kinds of music, namely personal preferred music and neutral music. In analyzing the synchronization signals of EEG and fNIRS, we found that music promotes the activity of the brain (especially the prefrontal lobe), and the activation induced by preferred music is stronger than that of neutral music. For the multi-modal features of EEG and fNIRS, we proposed an improved Normalized-ReliefF method to fuse and optimize them and found that it can effectively improve the accuracy of distinguishing between the brain activity evoked by preferred music and neutral music (up to 98.38%). Our work provides an objective reference based on neuroimaging for the research and application of personalized music therapy.

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Rough sets have been widely used in the fields of machine learning and feature selection. However, the classical rough sets have the problems of difficultly dealing with real-value data and weakly fault tolerance. In this paper, by introducing a neighborhood rough set model, the values of decision systems are granulated into some condition and decision neighborhood granules. A concept of neighborhood granular swarm is defined in a decision system. Then the sizes of a neighborhood granule and a neighborhood granular swarm are also given. In order to enhance the fault-tolerant ability of classification systems, we define some concepts of granule inclusion, variable precision neighborhood approximation sets and positive region. We propose a variable precision neighborhood rough set model, and analyze its property. Furthermore, based on the positive region of a variable precision neighborhood, we give the significance of an attribute and use it to select feature subsets. A feature subset selection algorithm to the variable precision neighborhood rough sets is designed. Finally, the feature selection algorithm is carried out on the UCI datasets, and the selected features are tested by the support vector machine (SVM) classification algorithm. Theoretical analysis and experiments show that the proposed method can find the effective and compact feature subsets, which have abilities of fault tolerance.

The random forest is a combined classification method belonging to ensemble learning. The random forest is also an important machine learning algorithm. The random forest is universally applicable to most data sets. However, the random forest is difficult to deal with uncertain data, resulting in poor classification results. To overcome these shortcomings, a broad granular random forest algorithm is proposed by studying the theory of granular computing and the idea of breadth. First, we granulate the breadth of the relationship between the features of the data sets samples and then form a broad granular vector. In addition, the operation rules of the granular vector are defined, and the granular decision tree model is proposed. Finally, the multiple granular decision tree voting method is adopted to obtain the result of the granular random forest. Some experiments are carried out on several UCI data sets, and the results show that the classification performance of the broad granular random forest algorithm is better than that of the traditional random forest algorithm.