A Novel Methodology to study the Cognitive Load Induced EEG Complexity Changes: Chaos, Fractal and Entropy based approach

Abstract

BackgroundDynamic Systems Theory (DST) can provide both the conceptual framework and literal description of the underlying complexity dynamics associated with human cognition, specifically during information processing of the brain under the effect of an external stimuli. Proposed MethodTo study the complexity changes during cognitive loading of the brain using Largest Lyapunov Exponent (LLE), Higuchi Fractal Dimension (HFD) and Sample Entropy (SampEn) as a multiparametric signature of cognitive processing. The proposed methodology demonstrates joint Time-Space representation of the various Brain Rhythms under four different classes of Cognitive Tasks (Emotion, Focus, Memory and Problem Solving) given to four subjects. The raw EEG signal is acquired using a 19 channel EEG machine, denoised using Wavelet packet decomposition technique. Brain waves are extracted using the scalogram plot. The parameters are calculated for each channel over a 2 min analysis window sliding through the whole length. ResultsThese parameters were able to classify between different cognitive states, such as Emotion, Focus, Memory and Problem Solving with an accuracy of 99%. Comparison of Existing MethodPrevious works haven’t addressed complexity changes during cognitive processing using DST. Earlier studies explain average topographical map of the brain for a fixed time window where as, we have presented the topographical map over a customizable fixed time sliding window. ConclusionThe cubic representation of the brain map containing non-linear parameters can prove to be a significant visualization tool for monitoring effects of cognitive loading using DST proponents as biomarker.