Research on Emotional Recognition of Peripheral Signals Assisted by EEG Signals Based on KDCCA
Multi signal fusion is a key focus in emotional recognition of p hysiological signals,among which physiological signals and electroencephalogram(EEG)signals are widely used.However,obtaining EEG signals is difficult and costly.In order to use EEG signals more effectively,a sentiment classification method based on kernel discriminant canonical correlation analysis(KDCCA)for EEG signal assisted physiological signals is proposed.During training,various signals are first extracted,and KDCCA is used to create a new discriminative space with the assistance of EEG signals.Then,various machine learning methods are used to construct sentiment models,and finally,only EEG signals are used during testing.After verification,the proposed method achieved better classification performance.
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