Visualizing brain signals in mental calculation by using Electroencephalography Topographic Map Animation with Independent component analysis perspectives

Abstract Cognitive Neuroscience is a scientific study area that examines biology processes and features that underlie cognition, focusing particularly on brain neural connections involved in mental processes. This research field discovers how the brain works when we perform a specific cognitive task such as doing school tests or brain games, from which experts can analyze and evaluate how they affect our brain. EEG signals are an essential tool in the mental process for detecting and monitoring the variations and patterns in the brain’s electrical activity. In this study, we aim to try to observe electrical activity through brain mapping animation of the brain using EEG signals recorded from participants while they performed mental calculations. The data involved thirty-six participants divided into good performers and poor performers based on their number of mental arithmetic tasks . We hypothesized that there would be changes in the distribution of EEG activity before and during performing cognitive activity in good and poor performers. The data were preprocessed, segmented into 2-second epochs, and decomposed into independent components (ICs) using ICA (independent component analysis). We used K-means clustering to obtain nine notable original brain signals based on clusters containing ICs from more than half of the subjects criteria. We applied spectral power to detect the change in theta (4-8 Hz), alpha (8-12 Hz), low beta (12-20 Hz), high beta (20-30 Hz) and gamma (30-4 5Hz) in rest and task conditions. event-related spectral perturbation, or ERSP (event-related shifts in the spectral power) was employed to divide two groups. Topographic map animations were created from the 2-second epochs to visualize the changes in brain activity over time. We expect this approach will allow us to observe the evolution of electrical brain activity during mental calculation through saturation of colorbar, particularly in regions associated with cognitive and numerical processing. We also anticipate observing differences in brain dynamics between individuals with varying arithmetic abilities. This study has the potential to provide a novel tool for investigating brain dynamics during mental arithmetic and may have applications in assessing and improving effectiveness of cognitive activity.