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

  • سال انتشار: 1401
  • محل انتشار: مجله فیزیک و مهندسی پزشکی، دوره: 12، شماره: 2
  • کد COI اختصاصی: JR_JBPE-12-2_007
  • زبان مقاله: انگلیسی
  • تعداد مشاهده: 59
دانلود فایل این مقاله

نویسندگان

- -

PhD, Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

- -

PhD, 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 ۲۹ subjects in ۶۰ trial is approximately ۸۴% in Mu (۸−۱۲ Hz) - Beta۱ (۱۲−۱۵ 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.

کلیدواژه ها

Electroencephalography, Motor Imagery, Effective Connectivity, Machine Learning, Brain-Computer Interfaces

اطلاعات بیشتر در مورد COI

COI مخفف عبارت CIVILICA Object Identifier به معنی شناسه سیویلیکا برای اسناد است. COI کدی است که مطابق محل انتشار، به مقالات کنفرانسها و ژورنالهای داخل کشور به هنگام نمایه سازی بر روی پایگاه استنادی سیویلیکا اختصاص می یابد.

کد COI به مفهوم کد ملی اسناد نمایه شده در سیویلیکا است و کدی یکتا و ثابت است و به همین دلیل همواره قابلیت استناد و پیگیری دارد.