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In this paper, we investigate stable patterns of electroencephalogram (EEG) over time for emotion recognition using a machine learning approach. Up to now, various findings of activated patterns associated with different emotions have been reported. However, their stability over time has not been fully investigated yet. In this paper, we focus on(More)
In recent years, there are many great successes in using deep architectures for unsupervised feature learning from data, especially for images and speech. In this paper, we introduce recent advanced deep learning models to classify two emotional categories (positive and negative) from EEG data. We train a deep belief network (DBN) with differential entropy(More)
EEG-based emotion recognition has been studied for a long time. In this paper, a new effective EEG feature named differential entropy is proposed to represent the characteristics associated with emotional states. Differential entropy (DE) and its combination on symmetrical electrodes (Differential asymmetry, DASM; and rational asymmetry, RASM) are compared(More)
Addressing the structural and functional variability between subjects for robust affective brain-computer interface (aBCI) is challenging but of great importance, since the calibration phase for aBCI is time-consuming. In this paper, we propose a subject transfer framework for electroencephalogram (EEG)-based emotion recognition via component analysis. We(More)
Various studies have shown that the traditional electrooculograms (EOGs) are effective for driving fatigue detection. However, the electrode placement of the traditional EOG recording method is around eyes, which may disturb the subjects' activities, and is not convenient for practical applications. To deal with this problem, we propose a novel electrode(More)
In this demo paper, we designed a novel framework combining EEG and eye tracking signals to analyze users' emotional activities in response to multimedia. To realize the proposed framework, we extracted efficient features of EEG and eye tracking signals and used support vector machine as classifier. We combined multimodel features using feature-level fusion(More)
This study aims at finding the relationship between EEG signals and human emotional states. Movie clips are used as stimuli to evoke positive, neutral and negative emotions of subjects. We introduce a new effective classifier named discriminative graph regularized extreme learning machine (GELM) for EEG-based emotion recognition. The average classification(More)
EEG signals, which can record the electrical activity along the scalp, provide researchers a reliable channel for investigating human emotional states. In this paper, a new algorithm, manifold regularized extreme learning machine (MRELM), is proposed for recognizing human emotional states (positive, neutral and negative) from EEG data, which were previously(More)
A DNA segment with DNA methylation site was detected from rice callus with or without 5-azaC treatment by MSAP. This segment was located on the first exon of gene OsMAPK2 and its 5' non-coding region. Gene OsMAPK2 had a CpG island in the 5' region and was homologous to AtMAPK12. Real-time quantitative PCR and Hpa II-McrBC PCR were conducted to detect the(More)
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