Brain Mapping and Interpretation of Reading Processing in Children Using EEG and Multivariate Statistical Analysis

Abstract

Difficulties in learning to read may have a number of causes and children tend to experience on the phonological route the most common disturbance in this cognitive task. Using two sample groups of children with and without reading difficulties and their corresponding EEG signals captured during the reading processing, we describe in this work a set of techniques that investigates such disturbance by generating whole brain mappings based on the entropy of each EEG electrode and non-supervised and supervised multivariate statistical analyses. Our experimental results have clearly showed specific neural organizations well suited to interpreting the word/phrase reading processing in these children. We believe that these techniques might become an effective computational tool in helping the diagnostic process of children with learning disabilities.

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

“…Thereby, we may assume that NO group organized its neural network on 3 distinct manners consecutively, while LD group did it only on 2 distinct manners. Here, we replicated our results from a smaller sample of these same groups on reading tasks [26], where we also found 3 factors for the control group and only 2 for the learning-disabled children.…”

“…Here, we propose the use of multivariate statistical analysis to process the electroencephalographic signal recorded during the solving of a set of language and mathematics tasks by two groups of children, one with learning disabilities (LD) from a community in condition of social and economic vulnerability, as considered by the municipality authorities, and other with no learning difficulties (NO) from a regular community. A small sample of these groups was already studied in our previous work focusing on reading skills [26].…”

“…Children solved the tasks while their EEG signals were registered using 20 electrodes placed according to the 10/20 protocol; impedance below 10 Kohm; low-pass filter 50Hz; sampling frequency of 256 Hz and 16 bits of resolution. Statistical analysis of their performance and the recorded EEG signals provided the data to investigate the linguistics and mathematics abilities, generating whole brain mappings based on the entropy of each EEG electrode [26,27,32] and on non-supervised and supervised multivariate techniques. The technique of EEG summarization is not new and has been successfully applied to study neural plasticity [32], arithmetic brain processing [27], dermatological treatment satisfaction [33], election decision making [34], DIPS model [35] and, more recently, moral dilemma [36], medical diagnosis [37] and reading process [26].…”

“…In particular, let a Nxn data matrix X be composed of input N signals (or trials) with n variables (or electrodes). This means that each column of matrix X represents the EEG summarization of a particular electrode H (ei ) observed all over the N trials [26]. Let this data matrix X have sample correlation matrix R with respectively Ρ and Λ eigenvector and eigenvalue matrices, that is,…”

“…DIPS was first developed in the field of Artificial Intelligence to formalize systems comprised of multiple agents that have individual expertise in solving defined problems but gain the ability to solve tasks of greater complexity through cooperation. DIPS intelligence is, therefore, a function of the types of tools used by its agents, as well as how and for what purpose these tools are used [26][27][28][29][30]. More specifically, we may propose that in the brain, DIPS knowledge is distributed among its agents or neurons according to their specialization, and is primarily encoded by the relationships or connections shared by these agents or neurons.…”