Accurately and automatically recognizing the emotional states of human beings is the central task in affective computing. The electroencephalography (EEG) data, generated from the neural activities in brain cortex, provide us with a reliable data source to perform emotion recognition. Besides the recognition accuracy, it is also necessary to explore the importance of different EEG features, frequency bands, and channels in emotion expression. In this article, we propose a unified framework termed graph-regularized least square regression with feature importance learning (GFIL) to simultaneously achieve these goals by incorporating an autoweighting variable into the least square regression. Unlike the widely used trial-and-error manner, GFIL automatically completes the identification once it is trained. Specifically, GFIL can: 1) adaptively discriminate the contributions of different feature dimensions; 2) automatically identify the critical frequency bands and channels; and 3) quantitatively rank and select the features by the learned autoweighting variable. From the experimental results on the SEED_IV data set, we find GFIL obtained improved accuracies based on the feature autoweighting strategy, which are 75.33%, 75.03%, and 79.17% corresponding to the three cross-session recognition tasks (session1->session2, session1->session3, session2->session3), respectively. Additionally, the Gamma band is identified as the most important one and the channels locating in the prefrontal and left/right central regions are more important.

Multimodal image fusion plays an essential role in medical image analysis and application, where computed tomography (CT), magnetic resonance (MR), single-photon emission computed tomography (SPECT), and positron emission tomography (PET) are commonly-used modalities, especially for brain disease diagnoses. Most existing fusion methods do not consider the characteristics of medical images, and they adopt similar strategies and assessment standards to natural image fusion. While distinctive medical semantic information (MS-Info) is hidden in different modalities, the ultimate clinical assessment of the fusion results is ignored. Our MsgFusion first builds a relationship between the key MS-Info of the MR/CT/PET/SPECT images and image features to guide the CNN feature extractions using two branches and the design of the image fusion framework. For MR images, we combine the spatial domain feature and frequency domain feature (SF) to develop one branch. For PET/SPECT/CT images, we integrate the gray color space feature and adapt the HSV color space feature (GV) to develop another branch. A classification-based hierarchical fusion strategy is also proposed to reconstruct the fusion images to persist and enhance the salient MS-Info reflecting anatomical structure and functional metabolism. Fusion experiments are carried out on many pairs of MR-PET/SPECT and MR-CT images. According to seven classical objective quality assessments and one new subjective clinical quality assessment from 30 clinical doctors, the fusion results of the proposed MsgFusion are superior to those of the existing representative methods.