EEG Brain Mapping of Normal and Learning Disabled Children Using Factor and Linear Discriminant Analyses

Abstract

Objective: To use multivariate statistical analysis to process the electroencephalographic (EEG) signal recorded during the solving of language and mathematics tasks by two groups of children, with (LD) and without (NO) learning disabilities, and thus, to understand the possible difference in the neural circuit organization between these two groups. Methods: We processed the EEG data using an algorithm that summarize all the information about the possible sources of the EEG signal, recorded by each of our 20 electrodes, into a unique variable. Factor Analysis (FA) was used to study the covariance of this variable and Linear Discriminant Analysis (LDA) to separate samples of distinct groups. Results: FA disclosed 3 factors to group NO and 2 for group LD. The electrodes, grouped on each factor of each group are quietly different. LDA disclosed a frontal/posterior differentiation between NO and LD groups, grouping mainly the frontal and central electrodes on NO group and the occipital and posterior temporal electrodes on the LD group. Conclusion: Results clearly differentiate both groups of children, showing a stronger participation of (pre)frontal and central regions of the brain on normal children, whereas learning disabled children showed the use of frontal, temporal and occipital areas.