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

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 L ₂ 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 L _1/2 norm to solve this problem. However, the conventional sparse Granger causality analysis mod

SUBMITTER: Chen D 

PROVIDER: S-EPMC8358835 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature