Abstract
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%.
Highlights
Emotions are an important part of decision cognition and interpersonal interaction (Oatley et al, 2006; Izard, 2013), and research in many fields is attempting to recognize human emotions through computer systems, such as emotional computing, neurology, and psychology (Catanzarite and Greenburg, 1979; Picard, 1999)
Zhang et al used the Granger causality analysis model to construct an effective brain connection network on Database for Emotion Analysis Using Physiological Signals (DEAP) emotional EEG data to study how emotion affects the pattern of effective connection (Zhang et al, 2017); Coito et al used the Granger causality model to study whether the EEG phase of patients with left temporal lobe epilepsy and right temporal lobe epilepsy exhibited changes in directional functional connectivity (Coito et al, 2016)
Due to the sparse connectivity of the brain network, researchers proposed Granger causality analysis models based on the least absolute shrinkage and selection operator (LASSO) to solve the noise problem (Valdés-Sosa et al, 2005; Marinazzo et al, 2008; Shaw and Routray, 2018)
Summary
Emotions are an important part of decision cognition and interpersonal interaction (Oatley et al, 2006; Izard, 2013), and research in many fields is attempting to recognize human emotions through computer systems, such as emotional computing, neurology, and psychology (Catanzarite and Greenburg, 1979; Picard, 1999). Clinical and neuroscience applications will inevitably produce outliers or artifacts when collecting data (Blankertz et al, 2007) These can cause the quality of EEG signals to deteriorate and produce problems with noise. The original Granger causality analysis uses the L2 norm loss function, the squared nature of which tends to exaggerate outliers, and retains all of the data. This can lead to erroneous analysis results (Xu et al, 2007, 2010a; Li et al, 2015; Bore et al, 2018, 2019). Granger causality analysis based on the L1/2 norm has been developed, and experiments have proved that this obtains better solutions (Bore et al, 2020)
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