Feature Optimization for Gifted Children Diagnosis

This paper deals with the diagnosis of intellectual precocity in gifted children (GC) cases. The P300 component is usually used for giftedness identification. By the use of empirical mode decomposition (EMD), a significant P300 detection is obtained through electroencephalogram signals (EEG). The novelty of the proposed work is to speed up the intellectual ability characterization based on statistical features extraction from P300 response. In order to get an optimized number of estimated information, a selection technique based on the characterization degree criterion (CD-J) is then introduced. This allows a considerably computing time decreasing and an excessive performance of the achieved results. Besides that, the proposed analysis method is applied on (GC) dataset, covering a parental relationship. Compared to the previous works, the proposed approach seems to be promising and useful for the characterization children and their diagnostic improvement.