Analyzing EEG Patterns in Different Lobes During Rest and Critical Thinking States

This study explores the neural correlates of critical thinking by analyzing electroencephalogram (EEG) patterns across different brain lobes during resting and cognitive task conditions. Specifically, the research investigates variations in alpha, beta, and gamma frequency bands to identify EEG signatures associated with higher-order cognitive processes. Ten healthy participants (aged 20–27 years, both male and female) completed four activities: a resting task with eyes closed and minimal movement, followed by three critical thinking tasks (Sets A, B, and C), each comprising ten brain teaser questions. EEG data were acquired using a 14-channel mobile EEG system (EMOTIV EPOC +). Signal preprocessing involved applying a Butterworth bandpass filter. Feature extraction utilized both linear (Power Spectral Density via Welch and Burg methods) and nonlinear (convolutional operations) approaches to compute statistical features including maximum, minimum, mean, median, mode, standard deviation, and variance. Subsequently, classification was performed using Decision Tree (DT), K-Nearest Neighbor (KNN), and Multi-Layer Perceptron (MLP) algorithms. The findings reveal that the combination of the Welch method with KNN and a subset of statistical features yields the highest classification accuracy, particularly in the beta and gamma bands. Notably, gamma band activity from the frontal lobe achieved the highest classification accuracy of 89.7%, indicating that the frontal region plays a critical role in supporting cognitive processes associated with critical thinking.