LAUSR

Introduction: Mental arithmetic analysis based on Electroencephalogram (EEG) signal for monitoring the state of the user’s brain functioning can be helpful for understanding some psychological disorders such as attention deficit hyperactivity disorder, autism spectrum disorder, or dyscalculia where the difficulty in learning or understanding the arithmetic exists. Most mental arithmetic recognition systems rely on features of a single channel of EEG; however, the relationships among EEG channels in the form of effective brain connectivity analysis can contain valuable information. The aim of this paper is to identify a set of discriminative effective brain connectivity features from EEG signal and develop a hierarchical feature selection structure for classification of mental arithmetic and baseline tasks effectively. Methods: We estimated effective connectivity using Directed Transfer Function (DTF), direct Directed Transfer Function (dDTF) and Generalized Partial Directed Coherence (GPDC) methods. These measures determine the causal relation between different brain areas. To select most significant effective connectivity features, a hierarchical feature subset selection method is used. First Kruskal–Wallis test was performed and consequently, five feature selection algorithms namely Support Vector Machine ( SVM ) method based on Recursive Feature Elimination, Fisher score, mutual information, minimum Redundancy Maximum Relevance and concave minimization and SVM are used to select the best discriminative features. Finally, SVM method was used for classification. Results: Results show that the best EEG classification performance in 29 participants and 60 trials is obtained using GPDC and feature selection via concave minimization method in Beta2 (15−22Hz) frequency band with 89% accuracy. Conclusion: This new hierarchical automated system could be useful for discrimination of mental arithmetic and baseline tasks from EEG signal effectively.