Classification of Subliminal Affective Priming Effect Based on AE and SVM

The study of the Subliminal Affective Priming Effect (SAPE) mainly uses event-related potential technology and mapping method. Many researches are only for the study of emotional classification, but there are few researches on the classification of the SAPE. That is, the SAPE is directly judged by the psychologist in most experiment. So, this paper designs a classifier based on Automatic Encoder (AE) and Support Vector Machine (SVM) for automatic recognition of SPAE. Initially, this paper collects EEG signal, and then extracts statistical features from EEG signal to form a data set. After that, the data set is dimension reduction by AE and then divided into training set and test set randomly. At last, the already designed model is trained with the training set and validated with the test set. In the experiment, we find that the designed classifier has the best performance compared with the classifiers based on BP neural network, Principal Component Analysis (PCA) and SVM. The experimental results show that the average classification accuracy is 95.31%. The classification results further indicate that the SAPE’s judgment is hopeful to reduce the labor with the machine.