Functional Connectivity Evaluation for Infant EEG Signals based on Artificial Neural Network

The employment of the brain signals
electroencephalography (EEG) could supply a deep intuitive
understanding for infants behaviour and their alertness level
within the living environment. The study of human brain
through a computer-based approach has increased significantly
as it aiming at the understanding of infants’ mind and measure
their attention towards the surrounding activities. The artificial
neural network achieved a significant level of success in different
fields such as pattern classification, decision making, prediction,
and adaptive control by learning from a set of data and construct
weight matrices to represent the learning patterns. This research
study proposes an artificial neural network based approach to
predict the rightward asymmetry or leftward asymmetry which
reflects higher frontal functional connectivity in the frontal right
and frontal left, respectively within infant’s brain. In the
traditional methods, the value of asymmetry of the frontal (FA)
functional connectivity is used to determine the rightward or the
leftward asymmetry. While the proposed approach is trying to
predict that without going through all the levels of the calculation
complexity. The achieved work will supply a deep understanding
into the deployment of the functional connectivity to provide
information on the interactions between different brain regions.

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