Mental Arithmetic Task Recognition Using Effective Connectivity and Hierarchical Feature Selection From EEG Signals.

Brain analysis methods by Electroencephalogram (EEG) signals provide a suitable method to monitor human brain activity due to having high temporal resolution, being noninvasive, inexpensive, and portable method. Analysis of mental arithmetic based EEG signal is helpful for psychological disorders like dyscalculia where they have learning understanding arithmetic, attention deficit hyperactivity, and autism spectrum disorders with attention deficit problem. This study finds distinctive effective brain connectivity features and creates a hierarchical feature selection for classification of mental arithmetic and baseline tasks effectively. Best EEG classification performance in 29 participants and 60 trials is obtained using Generalized Partial Directed Coherence (GPDC) methods and feature selection via concave minimization method in Beta2 (15-22Hz) frequency band with 89% accuracy. Thus, this new hierarchical automated system is useful for discrimination of mental arithmetic and baseline tasks from EEG signal effectively.