Abstract
The objective of this study is to detect affective response of children to facial expression based on alpha power density of brain activity. Electroencephalography data were collected from 10 typical children. The alpha power temporal information of active brain regions was extracted. Performance of the power spectrum feature was evaluated in emotion recognition process using K nearest neighbor, a regularized least square and multilayer perceptron classifier. A statistical analysis indicated right alpha activity during negative and calm emotional states. Statistical results showed significant difference between rest conditions and emotional state. The best accuracy we got to detect emotional states is by using regularized least square that is 70%.
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