Multi-Frequent Band Collaborative EEG Emotion Classification Method Based on Optimal Projection and Shared Dictionary Learning.

Affective computing is concerned with simulating people's psychological cognitive processes, of which emotion classification is an important part. Electroencephalogram (EEG), as an electrophysiological indicator capable of recording brain activity, is portable and non-invasive. It has emerged as an essential measurement method in the study of emotion classification. EEG signals are typically split into different frequency bands based on rhythmic characteristics. Most of machine learning methods combine multiple frequency band features into a single feature vector. This strategy is incapable of utilizing the complementary and consistent information of each frequency band effectively. It does not always achieve the satisfactory results. To obtain the sparse and consistent representation of the multi-frequency band EEG signals for emotion classification, this paper propose a multi-frequent band collaborative classification method based on optimal projection and shared dictionary learning (called MBCC). The joint learning model of dictionary learning and subspace learning is introduced in this method. MBCC maps multi-frequent band data into the subspaces of the same dimension using projection matrices, which are composed of a common shared component and a band-specific component. This projection method can not only make full use of the relevant information across multiple frequency bands, but it can also maintain consistency across each frequency band. Based on dictionary learning, the subspace learns the correlation between frequency bands using Fisher criterion and principal component analysis (PCA)-like regularization term, resulting in a strong discriminative model. The objective function of MBCC is solved by an iterative optimization algorithm. Experiment results on public datasets SEED and DEAP verify the effectiveness of the proposed method.

Optimized Projection and Fisher Discriminative Dictionary Learning for EEG Emotion Recognition.

Electroencephalogram (EEG)-based emotion recognition (ER) has drawn increasing attention in the brain-computer interface (BCI) due to its great potentials in human-machine interaction applications. According to the characteristics of rhythms, EEG signals usually can be divided into several different frequency bands. Most existing methods concatenate multiple frequency band features together and treat them as a single feature vector. However, it is often difficult to utilize band-specific information in this way. In this study, an optimized projection and Fisher discriminative dictionary learning (OPFDDL) model is proposed to efficiently exploit the specific discriminative information of each frequency band. Using subspace projection technology, EEG signals of all frequency bands are projected into a subspace. The shared dictionary is learned in the projection subspace such that the specific discriminative information of each frequency band can be utilized efficiently, and simultaneously, the shared discriminative information among multiple bands can be preserved. In particular, the Fisher discrimination criterion is imposed on the atoms to minimize within-class sparse reconstruction error and maximize between-class sparse reconstruction error. Then, an alternating optimization algorithm is developed to obtain the optimal solution for the projection matrix and the dictionary. Experimental results on two EEG-based ER datasets show that this model can achieve remarkable results and demonstrate its effectiveness.

Propofol suppresses proliferation and invasion of glioma cells by upregulating microRNA-218 expression.

Propofol (2,6-diisopropylphenol) is a commonly used intravenous anesthetic agent. The present study aimed to assess the effect of propofol on the proliferation and invasion of human glioma cells, and to determine the potential underlying molecular mechanisms. The effects of propofol on U373 glioblastoma cell proliferation, apoptosis and invasion were detected by an MTT assay, caspase­3 activity measurement and a Matrigel™ invasion assay, respectively. MicroRNA (miR)­218 expression and matrix metalloproteinase (MMP)­2 protein expression levels were analyzed by quantitative polymerase chain reaction and western blot analysis, respectively. In addition, miR­218 precursor was transfected into the cells to assess whether overexpression of miR­218 could affect MMP­2 expression. Anti­miR­218 was transfected into the cells to evaluate the role of miR­218 in the effects of propofol on the biological behavior of glioma cells. The results of the present study demonstrated that propofol significantly increased the expression levels of miR­218, inhibited U373 cell proliferation and invasion, and facilitated apoptosis. In addition, treatment with propofol efficiently reduced MMP­2 protein expression levels, and overexpression of miR­218 also decreased MMP­2 protein expression levels. Whereas, neutralization of miR­218 using the anti­miR-218 antibody reversed the effects of propofol on the biological behavior of U373 cells, and on the inhibition of MMP-2 protein expression. In conclusion, propofol may effectively suppress proliferation and invasion, and induce the apoptosis of glioma cells, at least partially through upregulation of miR-218 expression.