The aim of this study is to recognize human emotions by electroencephalographic (EEG) signals. The innovation of our research methods involves two aspects: First, we integrate the spatial characteristics, frequency domain, and temporal characteristics of the EEG signals, and map them to a two-dimensional image. With these images, we build a series of EEG Multidimensional Feature Image (EEG MFI) sequences to represent the emotion variation with EEG signals. Second, we construct a hybrid deep neural network to deal with the EEG MFI sequences to recognize human emotional states where the hybrid deep neural network combined the Convolution Neural Networks (CNN) and Long Short-Term-Memory (LSTM) Recurrent Neural Networks (RNN). Empirical research is carried out with the open-source dataset DEAP (a Dataset for Emotion Analysis using EEG, Physiological, and video signals) using our method, and the results demonstrate the significant improvements over current state-of-the-art approaches in this field. The average emotion classification accuracy of each subject with CLRNN (the hybrid neural networks that we proposed in this study) is 75.21%.

Brain informatics adopts a systematic methodology to study clinical diagnosis and pathology of mental disorders from macro, meso and micro points of view. Systematic studies produce multi-level and multi-aspect health big data of mental disorders. Effectively integrating multi-level mental health big data and providing multi-level and content-oriented data services for different types of users by an open and extendable mode becomes new requirements on the acquisition and computing of mental health big data. The existing Data-Brain-based brain data center cannot meet these new requirements. This paper proposes a brain and health big data center and develops a content-oriented cloud service architecture, namely, wisdom as a service, for realizing the brain and health big data center. The illustrative example demonstrates significance and usefulness of the proposed approach.

This study aims to build a smart hospital information framework to deal with various medical information. First, a ubiquitous smart hospital information system model under WaaS (Wisdom as a Service) architecture is proposed. With this model, medical information can be organized into different levels. In this model, novel methods of organizing medical information (offline computing) and smart services delivering (online computing) are defined. The methods are used to supply medical knowledge recommendation services to patients according to their personalized condition and context. As a use case, a smart medical knowledge recommendation system, namely SKeWa, was built to reveal the usefulness of the model and the methods.

In recent years, with the rapid growth of university scientific research achievements in number and quality, new standard and requirement for university scientific research management are brought up in China. University scientific research management should be people-oriented, and have two responsibilities, management and service, which are equally important. Practice has shown that the digitized and people-oriented Research Information Management System gives way to more orderly scientific research management. This paper describes the content and function of the Research Information Management System.