Volume 12, Issue 6 (November & December 2021)
BCN 2021, 12(6): 817-826 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Maghsoudi A, Shalbaf A. Mental Arithmetic Task Recognition Using Effective Connectivity and Hierarchical Feature Selection From EEG Signals. BCN 2021; 12 (6) :817-826
URL: http://bcn.iums.ac.ir/article-1-1610-en.html
URL: http://bcn.iums.ac.ir/article-1-1610-en.html
1- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
2- Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
2- Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Abstract:
Introduction: Mental arithmetic analysis based on Electroencephalogram (EEG) signals can help understand disorders, such as attention-deficit hyperactivity, dyscalculia, or autism spectrum disorder 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 between EEG channels in the form of effective brain connectivity analysis can contain valuable information. This study aims to find distinctive, effective brain connectivity features and create a hierarchical feature selection for effectively classifying mental arithmetic and baseline tasks.
Methods: We estimated effective connectivity using Directed Transfer Function (DTF), direct DTF (dDTF) and Generalized Partial Directed Coherence (GPDC) methods. These measures determine the causal relationship between different brain areas. A hierarchical feature subset selection method selects the most significant effective connectivity features. Initially, Kruskal–Wallis test was performed. Consequently, five feature selection algorithms, namely, Support Vector Machine (SVM) method based on Recursive Feature Elimination, Fisher score, mutual information, minimum Redundancy Maximum Relevance (RMR), and concave minimization and SVM are used to select the best discriminative features. Finally, the SVM method was used for classification.
Results: The obtained results indicated 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 helpful in the discrimination of mental arithmetic and baseline tasks from EEG signals effectively.
Methods: We estimated effective connectivity using Directed Transfer Function (DTF), direct DTF (dDTF) and Generalized Partial Directed Coherence (GPDC) methods. These measures determine the causal relationship between different brain areas. A hierarchical feature subset selection method selects the most significant effective connectivity features. Initially, Kruskal–Wallis test was performed. Consequently, five feature selection algorithms, namely, Support Vector Machine (SVM) method based on Recursive Feature Elimination, Fisher score, mutual information, minimum Redundancy Maximum Relevance (RMR), and concave minimization and SVM are used to select the best discriminative features. Finally, the SVM method was used for classification.
Results: The obtained results indicated 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 helpful in the discrimination of mental arithmetic and baseline tasks from EEG signals effectively.
Type of Study: Original |
Subject:
Computational Neuroscience
Received: 2019/09/23 | Accepted: 2020/04/20 | Published: 2021/11/1
Received: 2019/09/23 | Accepted: 2020/04/20 | Published: 2021/11/1
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
Copyright © The Author(s);
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC-By-NC), which permits use, distribution, and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
Contact Information
