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Maghsoudi, Arash, Shalbaf, Ahmad. (1401). Hand Motor Imagery Classification Using Effective Connectivity and Hierarchical Machine Learning in EEG Signals. سامانه مدیریت نشریات علمی, 12(2), 161-170. doi: 10.31661/jbpe.v0i0.1264
Arash Maghsoudi; Ahmad Shalbaf. "Hand Motor Imagery Classification Using Effective Connectivity and Hierarchical Machine Learning in EEG Signals". سامانه مدیریت نشریات علمی, 12, 2, 1401, 161-170. doi: 10.31661/jbpe.v0i0.1264
Maghsoudi, Arash, Shalbaf, Ahmad. (1401). 'Hand Motor Imagery Classification Using Effective Connectivity and Hierarchical Machine Learning in EEG Signals', سامانه مدیریت نشریات علمی, 12(2), pp. 161-170. doi: 10.31661/jbpe.v0i0.1264
Maghsoudi, Arash, Shalbaf, Ahmad. Hand Motor Imagery Classification Using Effective Connectivity and Hierarchical Machine Learning in EEG Signals. سامانه مدیریت نشریات علمی, 1401; 12(2): 161-170. doi: 10.31661/jbpe.v0i0.1264

Hand Motor Imagery Classification Using Effective Connectivity and Hierarchical Machine Learning in EEG Signals

Journal of Biomedical Physics and Engineering
مقاله 7، دوره 12، شماره 2، تیر 2022، صفحه 161-170    اصل مقاله (1.76 M)
نوع مقاله: Original Research
شناسه دیجیتال (DOI): 10.31661/jbpe.v0i0.1264
نویسندگان
Arash Maghsoudi1؛ Ahmad Shalbaf* 2
1PhD, Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
2PhD, Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
چکیده
Background: Motor Imagery (MI) Brain Computer Interface (BCI) directly links central nervous system to a computer or a device. Most MI-BCI structures rely on features of a single channel of Electroencephalogram (EEG). However, to provide more valuable features, the relationships among EEG channels in the form of effective brain connectivity analysis must be considered.
Objective: This study aims to identify a set of robust and discriminative effective connectivity features from EEG signals and to develop a hierarchical machine learning structure for discrimination of left and right hand MI task effectively.
Material and Methods: In this analytical study, we estimated effective connectivity using Granger Causality (GC) methods namely, Generalized Partial Directed Coherence (GPDC), Directed Transfer Function (DTF) and direct Directed Transfer Function (dDTF). These measures determine the transient causal relation between different brain areas. Then a feature subset selection method based on Kruskal–Wallis test was performed to choose most significant directed causal connection between channels. Moreover, the minimal-redundancy-maximal-relevance feature selection method is applied to discard non-significance features. Finally, support vector machine method is used for classification.
Results: The maximum value of the classification accuracies using GC methods over different frequency bands in 29 subjects in 60 trial is approximately 84% in Mu (8−12 Hz) - Beta1 (12−15 Hz) frequency band using GPDC method.
Conclusion: This new hierarchical automated BCI system could be applied for discrimination of left and right hand MI tasks from EEG signal, effectively.
کلیدواژه‌ها
Electroencephalography؛ Motor Imagery؛ Effective Connectivity؛ Machine Learning؛ Brain-Computer Interfaces
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