Classification of Resting and Cognitive States using EEG-based Feature Extraction and Connectivity Approach

Classification of resting and cognitive states has its importance in brain neuroscience for understating the underlying behaviors of cognition. The human brain is considered as a complex system having different mental states such as resting, active or cognitive states. It is a well-understood fact that the brain activity increases with the increased demand of cognition. In this paper, the cognitive and resting state classification based on EEG-based feature extraction and connectivity approaches are described. EEG-based connectivity approaches are a good discriminator for different mental states. EEG data were collected from 34 human participants at resting and during a learning state. After preprocessing, EEG-based feature extraction method and connectivity approach were implemented, and their results were classified. Results showed that the connectivity approach gave 79.90% accuracy while the highest accuracy achieved by feature extraction approach was 78.50%. It is concluded that EEG-based connectivity approach discriminates the resting and cognitive states more efficiently.