Abstract
To study the effect of the design of the experimental paradigm on the accuracy of emotion recognition based on picture-stimulated EEG signals. Using the comparison of three innovative models (random presentation, sequential presentation and independent presentation), we identified the experimental paradigm that had the highest impact on the accuracy of emotional recognition. We used by principal component analysis (PCA) and support vector machine (SVM) to extract and classify emotions separately. The results show that in the three experimental paradigms highest accuracy rate is when positive and negative emotion pictures are presented in sequence. The innovative model presented in the series has the highest accuracy and is more consistent with the dynamic process of emotion generation, ensuring that the evoked EEG signals imply complete emotional information. This research on the experimental paradigm is mostly convenient for follow-up scholars to continue to explore EEG signals.
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