EEG Based Network Connectivity Classification in 7 and 9 Years- Old Children.

Area of Science:

• Neuroscience
• Developmental Psychology
• Cognitive Science

Background:

• Understanding neural pathways in children requires insight into cognitive development.
• Cognitive processes vary significantly with age, assessed via stimulus recognition.
• Longitudinal electroencephalography (EEG) data from healthy children aged seven and nine years were collected.

Purpose of the Study:

• To investigate the development of the response conflict process in children.
• To assess network connectivity using coherence and its imaginary component in children.
• To evaluate the efficacy of classification algorithms in distinguishing age-related brain connectivity patterns.

Main Methods:

• Collected longitudinal EEG data from 45 healthy children at ages seven and nine.
• Administered Flanker stimuli (congruent and incongruent) across delta, theta, alpha, and beta frequency bands.
• Analyzed network connectivity using coherence and the imaginary component of coherency.
• Tested various classification algorithms to discriminate between age groups based on coherency data.

Main Results:

• Brain connectivity was more effective in distinguishing between the seven- and nine-year-old groups using incongruent Flanker stimuli.
• The imaginary part of coherency provided superior features for classification in the incongruent condition.
• A classification accuracy exceeding 94.31% was achieved using features from theta, alpha, and beta bands with a naïve Bayes classifier.

Conclusions:

• Network connectivity, especially using the imaginary component of coherency, is a valuable biomarker for tracking cognitive development in children.
• Incongruent stimuli in the Flanker task elicit more discriminative brain connectivity patterns related to age.
• Machine learning classifiers, like naïve Bayes, can effectively differentiate developmental stages of cognitive control based on EEG-derived brain connectivity features.