Abstract There are numerous studies measuring brain emotional status by analyzing EEGs under the emotional stimuli that have occurred, and acceptable accuracies were obtained in existing researches. However, emotion classification model would be challenged when it was applied in practical application. Shared non-emotional information in homologous samples may make the classification model easier to recognize the samples in the testing set, resulting in higher accuracies in EEG-based emotion recognition. In the pattern recognition, we proposed a new sample-divided method, named integrating homologous samples method, where the homologous samples were either used to build a classifier, or to be tested. In this paper, affective pictures and videos were used to elicit four emotional states of neutral, happy, sad, and disgust from 10 subjects, and EEG signals were recorded during the pictures or videos display. PSD were extracted from EEGs of 6 frequency bands (θ: 4-8 Hz, α: 8-12 Hz, β1: 13-18 Hz, β2: 18-30 Hz, γ1: 30-36 Hz and γ2: 36-44 Hz), and then sent to a SVM for classification. The results showed that the classification accuracy was much lower for the integrating homologous samples method (IHSM) than for the traditional dividing the samples randomly (TDSR). For the image evoked task, 43.92% and 34.15% were obtained by TDSR and IHSM, respectively. There were 94.45% and 37.88% for video evoked task. SVM-RFE was employed to select emotional features and improved the classification rates to 76.22% and 72.35% for these two tasks. The proposed method avoided the overinflated accuracies brought by the traditional method, and handle this problem is an important and necessary step from the laboratory to the practical application.
Liu Shuang,Tong Jingjing,Yang Jiajia, et al. Study on Emotion Recognition with Integrating EEG Homologous Samples Method[J]. Chinese Journal of Biomedical Engineering, 2016, 35(3): 272-277.
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