شناسایی حرکات تصور شده برمبنای ویژگی‌های دینامیکی سیگنال EEG

نوع مقاله: مقاله پژوهشی

نویسندگان

1 دانشکده مهندسی برق- واحد نجف‌آباد، دانشگاه آزاد اسلامی، نجف آباد، ایران

2 دانشکده مهندسی برق- ,واحد نجف آباد، دانشگاه آزاد اسلامی، نجف آباد، ایران

چکیده

کنترل اندام­های مصنوعی می­تواند از طریق تفکیک الگوهای تصورحرکت با استفاده ازسیگنال­های الکتروانسفالوگرافی (EEG) انجام شود. هدف از انجام این مطالعه تشخیص تصور حرکات دست و پا برمبنای سیگنال EEG است. مجموعه آزمون­های IVA از داده­های  BCI Competition IIIکه شامل سیگنال­های EEG ثبت­شده از 5 فرد سالم و در سه کانال C3، C4 و CZ است، برای طراحی سیستم تشخیص حرکات تصور شده به­کار رفت. در ابتدا، با استفاده از روش تحلیل مولفه­ی اصلی چند مقیاسی (MSPCA) اجزای اساسی نویز سیگنال EEG حذف شدند. در مرحله­ی بعد، سیگنال­های EEG با دو روش مختلف شامل فیلترینگ فرکانسی با استفاده از فیلتر باترورث و روش تبدیل بسته ویولت (WPT) به بخش­هایی تجزیه شدند. در این مطالعه، تجزیه ‌و تحلیل نوسانات تفکیک‌شده، بعد فرکتال، بعد همبستگی، پیچیدگی لیمپل-زیو و آنتروپی به­عنوان ویژگی­های دینامیکی برای سیگنال­ها محاسبه شدند. ویژگی­های مورد نظر در هر دو روش تجزیه، برای نسخه زمانی زیرباندهای تعیین شده محاسبه شدند. به­منظور تعیین بهترین عملکرد سیستم، ترکیب­های متفاوتی از کانال­ها و ویژگی­ها مورد ارزیابی قرار گرفتند. روش تجزیه بر مبنای تبدیل ویولت، درحالت استفاده از هر سه کانال و پنج ویژگی، بالاترین دقت تشخیص را ارایه کرد؛ به­گونه­ای که با استفاده از روش­ طبقه­بندی ماشین بردار پشتیبان (SVM)، دقت 93 درصد در شناسایی حرکات مورد نظر به­دست آمد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Recognition of Motor Imagery Based on Dynamic Features of EEG Signals

نویسندگان [English]

  • Negar Dashti 1
  • Mahdi Khezri 2
1 Department of Electrical Engineering, Najafabad Branch,, Islamic Azad University,, Najafabad, Iran
2 Department of Electrical Engineering, Najafabad Branch,, Islamic Azad University,, Najafabad, Iran
چکیده [English]

The control of artificial limbs can be done by distinguishing the patterns of imagined movement using the EEG signals. The aim of this study was to identify hand and foot imagery movements based on EEG signals. The Iva dataset of BCI Competition III, which includes EEG signals from 5 healthy individuals in C3, C4 and CZ channels, was used to design the imagery movements detection system. Initially, the basic components of EEG signal noise were removed using the MSPCA method. In the next step, the EEG signals were decomposed in two different ways including frequency filtering using the Butterworth filter and the wavelet packet transform (WPT). In this study, the detrended Fluctuation Analysis, Fractal dimension, Correlation dimension, Lempel-ziv complexity and Entropy as nonlinear dynamics features, were calculated for the signals. In both decomposition methods, the desired features were calculated for the temporal version of the specified subbands. In order to determine the best performance of the system, different combinations of the channels and the features were evaluated. The wavelet-based decomposition method, in the case of using all three channels and five features, provided the highest recognition accuracy; So that using support vector machine (SVM) classification method, the accuracy of 93% was obtained in identifying the desired movements.

کلیدواژه‌ها [English]

  • Motor imagery
  • Classification
  • Nonlinear features
  • Wavelet Transform
  • SVM
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