  Current trend in neurosciences is to use naturalistic stimuli, such as
cinema, class-room biology or video gaming, aiming to understand the brain
functions during ecologically valid conditions. Naturalistic stimuli recruit
complex and overlapping cognitive, emotional and sensory brain processes. Brain
oscillations form underlying mechanisms for such processes, and further, these
processes can be modified by expertise. Human cortical oscillations are often
analyzed with linear methods despite brain as a biological system is highly
nonlinear. This study applies a relatively robust nonlinear method, Higuchi
fractal dimension (HFD), to classify cortical oscillations of math experts and
novices when they solve long and complex math demonstrations in an EEG
laboratory. Brain imaging data, which is collected over a long time span during
naturalistic stimuli, enables the application of data-driven analyses.
Therefore, we also explore the neural signature of math expertise with machine
learning algorithms. There is a need for novel methodologies in analyzing
naturalistic data because formulation of theories of the brain functions in the
real world based on reductionist and simplified study designs is both
challenging and questionable. Data-driven intelligent approaches may be helpful
in developing and testing new theories on complex brain functions. Our results
clarify the different neural signature, analyzed by HFD, of math experts and
novices during complex math and suggest machine learning as a promising
data-driven approach to understand the brain processes in expertise and
mathematical cognition.
