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Current trend in neurosciences is to use naturalistic stimuli, such ascinema, class-room biology or video gaming, aiming to understand the brainfunctions during ecologically valid conditions. Naturalistic stimuli recruitcomplex and overlapping cognitive, emotional and sensory brain processes. Brainoscillations form underlying mechanisms for such processes, and further, theseprocesses can be modified by expertise. Human cortical oscillations are oftenanalyzed with linear methods despite brain as a biological system is highlynonlinear. This study applies a relatively robust nonlinear method, Higuchifractal dimension (HFD), to classify cortical oscillations of math experts andnovices when they solve long and complex math demonstrations in an EEGlaboratory. Brain imaging data, which is collected over a long time span duringnaturalistic stimuli, enables the application of data-driven analyses.Therefore, we also explore the neural signature of math expertise with machinelearning algorithms. There is a need for novel methodologies in analyzingnaturalistic data because formulation of theories of the brain functions in thereal world based on reductionist and simplified study designs is bothchallenging and questionable. Data-driven intelligent approaches may be helpfulin developing and testing new theories on complex brain functions. Our resultsclarify the different neural signature, analyzed by HFD, of math experts andnovices during complex math and suggest machine learning as a promisingdata-driven approach to understand the brain processes in expertise andmathematical cognition.

Nonlinear and Machine Learning Analyses on High-Density EEG data of Math Experts and Novices