EEG alpha and theta time-frequency structure during a written mathematical task

Area of Science:

• Neuroscience
• Cognitive Science
• Signal Processing

Background:

• Electroencephalography (EEG) offers insights into brain cognitive dynamics, but its application in real-world settings is evolving.
• Mathematical problem solving is a complex cognitive process not fully understood, with EEG studies often limited to clinical or controlled environments.
• Mobile EEG devices and advanced signal analysis techniques like matching pursuit (MP) enable sophisticated brain activity studies in non-clinical settings.

Purpose of the Study:

• To investigate electroencephalogram (EEG) activation patterns during mathematical (Math) problem solving using mobile EEG in a realistic environment.
• To apply a matching pursuit (MP)-based signal analysis technique to study induced EEG activity over extended periods.
• To analyze theta and alpha band activity during a Math task in healthy volunteers using a low-density stochastic MP dictionary.

Main Methods:

• Sixty healthy volunteers performed a written Math task while wearing a mobile EEG device (EPOC+) in a sitting position with eyes open.
• A matching pursuit (MP) signal analysis technique was employed on EEG data within 1-min windows.
• Analysis focused on theta [4 Hz-7.5 Hz] and alpha [7.5 Hz-13 Hz] frequency bands using a low-density stochastic MP dictionary.

Main Results:

• MP analysis decomposed EEG activity into MP atoms, with median durations around 3 seconds, comparable to event-related desynchronization/synchronization (ERS/ERD) studies.
• During Math tasks, significantly lower MP alpha atom/min was observed on the right side and posteriorly compared to Rest.
• MP theta atom/min was significantly higher on anterior electrodes, particularly on the left side, during Math tasks compared to Rest.

Conclusions:

• The study successfully replicated previous findings of EEG alpha and theta activation during Math tasks using a streamlined protocol with mobile EEG and MP analysis.
• MP analysis demonstrated efficiency in analyzing real-world, noisy EEG data, showing promise for studying mathematical cognition.
• Findings support the use of mobile EEG and MP techniques for investigating cognitive processes like mathematical problem solving in naturalistic settings.