Math Skills: a New Look from Functional Data Analysis.

Area of Science:

• Neuroscience
• Cognitive Science
• Biomedical Engineering

Background:

• Mental calculations engage multiple brain regions, primarily the left hemisphere's frontal, parietal, and temporal lobes.
• Cerebral activation patterns during mathematical tasks vary with individual competence, attention, and task difficulty.
• Functional Magnetic Resonance Imaging (fMRI) is effective but costly; Electroencephalography (EEG) presents a more accessible alternative for brain activity monitoring.

Purpose of the Study:

• To develop and validate an EEG-based method for differentiating cerebral activation levels during arithmetic tasks.
• To compare brain activity patterns between individuals with varying mathematical abilities performing the same subtraction task.

Main Methods:

• Utilized EEG to record brain activity during resting state and a subtraction task.
• Extracted regional brain activation levels from EEG data.
• Applied Functional Data Analysis (FDA) to statistically analyze biomedical signals as functions, comparing distinct subject groups.

Main Results:

• Successfully detected distinct cerebral activation patterns between groups with different arithmetic task performance.
• Identified significantly higher activation levels in the parietal lobe among individuals with lower performance in the subtraction task.

Conclusions:

• EEG, combined with FDA, provides a feasible and cost-effective approach to investigate neural correlates of mathematical ability.
• The findings suggest that increased parietal lobe engagement may be associated with cognitive load or compensatory mechanisms in less proficient individuals during mental calculations.