An Accurate Object Detector With Effective Feature Extraction by Intrinsic Prior Knowledge

Sparse Granger Causality Analysis Model Based on Sensors Correlation for Emotion Recognition Classification in Electroencephalography

Frontiers in Computational Neuroscience ◽

10.3389/fncom.2021.684373 ◽

2021 ◽

Vol 15 ◽

Author(s):

Dongwei Chen ◽

Rui Miao ◽

Zhaoyong Deng ◽

Na Han ◽

Chunjian Deng

Keyword(s):

Feature Extraction ◽

Prior Knowledge ◽

Granger Causality ◽

Affective Computing ◽

Causality Analysis ◽

Emotion Classification ◽

Analysis Model ◽

Granger Causality Analysis ◽

Model Based ◽

Eeg Data

In recent years, affective computing based on electroencephalogram (EEG) data has attracted increased attention. As a classic EEG feature extraction model, Granger causality analysis has been widely used in emotion classification models, which construct a brain network by calculating the causal relationships between EEG sensors and select the key EEG features. Traditional EEG Granger causality analysis uses the L2 norm to extract features from the data, and so the results are susceptible to EEG artifacts. Recently, several researchers have proposed Granger causality analysis models based on the least absolute shrinkage and selection operator (LASSO) and the L1/2 norm to solve this problem. However, the conventional sparse Granger causality analysis model assumes that the connections between each sensor have the same prior probability. This paper shows that if the correlation between the EEG data from each sensor can be added to the Granger causality network as prior knowledge, the EEG feature selection ability and emotional classification ability of the sparse Granger causality model can be enhanced. Based on this idea, we propose a new emotional computing model, named the sparse Granger causality analysis model based on sensor correlation (SC-SGA). SC-SGA integrates the correlation between sensors as prior knowledge into the Granger causality analysis based on the L1/2 norm framework for feature extraction, and uses L2 norm logistic regression as the emotional classification algorithm. We report the results of experiments using two real EEG emotion datasets. These results demonstrate that the emotion classification accuracy of the SC-SGA model is better than that of existing models by 2.46–21.81%.

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Structural Feature Extraction from Regular-Shaped Rigid Objects(2) - Enhanced by Prior Knowledge

2010 International Symposium on Computational Intelligence and Design ◽

10.1109/iscid.2010.144 ◽

2010 ◽

Author(s):

Lei Zhang ◽

Qingshan Fu ◽

Xingguo Zhang ◽

Guoran Hua ◽

Longbiao Zhu

Keyword(s):

Feature Extraction ◽

Structural Feature ◽

Prior Knowledge ◽

Rigid Objects

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Intrinsic Prior Knowledge Driven CICA fMRI Data Analysis for Emotion Recognition Classification

IEEE Access ◽

10.1109/access.2019.2915291 ◽

2019 ◽

Vol 7 ◽

pp. 59944-59950 ◽

Cited By ~ 2

Author(s):

Xiaoying Chen ◽

Weiming Zeng ◽

Yuhu Shi ◽

Jin Deng ◽

Yuan Ma

Keyword(s):

Data Analysis ◽

Emotion Recognition ◽

Prior Knowledge ◽

Fmri Data ◽

Fmri Data Analysis ◽

Intrinsic Prior

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An Improved Quantitative Image Analyser

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100078663 ◽

1977 ◽

Vol 35 ◽

pp. 226-227

Author(s):

J.P. Fallon ◽

P.J. Gregory ◽

C.J. Taylor

Keyword(s):

Feature Extraction ◽

Quantitative Analysis ◽

Frequency Content ◽

General Sense ◽

Physical Measurements ◽

Quantitative Image ◽

Image Analyser ◽

Automatic Methods ◽

Quantitative Results

Quantitative image analysis systems have been used for several years in research and quality control applications in various fields including metallurgy and medicine. The technique has been applied as an extension of subjective microscopy to problems requiring quantitative results and which are amenable to automatic methods of interpretation.Feature extraction. In the most general sense, a feature can be defined as a portion of the image which differs in some consistent way from the background. A feature may be characterized by the density difference between itself and the background, by an edge gradient, or by the spatial frequency content (texture) within its boundaries. The task of feature extraction includes recognition of features and encoding of the associated information for quantitative analysis.Quantitative Analysis. Quantitative analysis is the determination of one or more physical measurements of each feature. These measurements may be straightforward ones such as area, length, or perimeter, or more complex stereological measurements such as convex perimeter or Feret's diameter.

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