Handwriting dynamic features and brain electrical activity related to graphic rule based on principal component analysis

Abstract

For handwriting difficulties, many have independently studied the dynamic feature of handwriting process and brain neural activity with respect to the graphics rule. However, a very little study has been done to analysis these two factors concurrently. Thus, this study aimed to determine the most significant parameter in differentiating the preferred (good hand writer) and non-preferred (poor hand writer) based on both dynamic features of drawing process and brain electrical activity by using Principal Component Analysis (PCA). Children's Visual-Motor Integration (VMI) skills were assessed by instructing him/her to freehand copy eleven geometrical figures with different figure's complexity. The dynamic feature of handwriting process and electroencephalogram (EEG) signal were recorded concurrently during drawing tasks. A total of 233 parameters were extracted, and PCA was applied to obtain low dimensional subspace of parameters. It was found that the most significant parameter was a high gamma band of the occipital area, O2 during tracing activity of a square shape. It is known that those employed, preferred graphics rules are good handwrites and has better academic performance. Hence, it is proposed that that employed, preferred graphics rule has better visual processing as one indicator for better academic performance. Meanwhile, the dynamic features showed less significant association with the graphics rule.