Nonlinear and machine learning analyses on high-density EEG data of math experts and novices

Abstract

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 functions 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 functions 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.

Approaching Electroencephalographic Pathological Spikes in Terms of Solitons

“…Solitons require a dissipative physical medium characterized by a certain degree of nonlinearity. In keeping with this claim, nervous nonlinear dynamics have been ubiquitously detected in both experimental settings and ecologically valid conditions [23,24]. Using brain network analysis, data-driven approaches and machine learning [25], various types of nonlinear dynamic behavior have been found in the wave series extracted from EEG data sets [23].…”