آزمون دروغ‌سنجی بر اساس پردازش آشوبناک سیگنال الکتروانسفالوگرام مبتنی بر نگاشت بازرخداد فازی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد، دانشکده علوم و فناوری‌های نوین، دانشگاه سمنان، سمنان، ایران

2 استادیار، دانشکده علوم و فناوری‌های نوین، دانشگاه سمنان، سمنان، ایران

3 استادیار، دانشکده مهندسی پزشکی، دانشگاه صنعتی همدان، همدان، ایران

چکیده

آزمون دانش گناهکار مبتنی بر سیگنال الکتروانسفالوگرام، یکی از پرکاربردترین روش‌های دروغ‌سنجی به شمار می‌رود. نگاشت بازرخداد به‌عنوان یکی از روش‌های پردازش آشوبناک در دروغ‌سنجی مورد استفاده قرار گرفته است. از جمله چالش‌های مهم این روش، انتخاب آستانه مناسب برای تعیین وقوع بازرخداد حالات سامانه در فضای فاز است که انتخاب نامناسب آن کارایی این روش‌ را تحت تأثیر قرار می‌دهد. در این مقاله به‌منظور حل این چالش از نگاشت بازرخداد فازی استفاده شده است. این نگاشت، تک‌ثبت‌های سیگنال الکتروانسفالوگرام را به تصویر بافت خاکستری تبدیل می‌کند. سپس ویژگی‌های‌ بافت تصویر بر اساس روش ماتریس رخداد هم‌زمان درجه خاکستری استخراج و با استفاده از مدل K-نزدیک‌ترین همسایگی طبقه‌بندی می‌شود. نتایج حاصل از طبقه‌بندی این بردار ویژگی با طول ۴ با صحت ۹۰ درصد بیانگر برتری این روش نسبت به روش متداول نگاشت بازرخداد با طول بردار ویژگی ۱۳ است. این کاهش بعد در بردار ویژگی منجر به افزایش سرعت آموزش، آزمون و تعمیم‌پذیری طبقه‌بند K-نزدیک‌ترین همسایگی به‌عنوان یک طبقه‌بند تنبل می‌شود. علاوه بر این، رویکرد پردازش تک ثبت مبتنی بر سوژه که در این مقاله درنظر گرفته شده است نیاز به وجود مجموعه داده‌ای‌ از سوژه‌های مختلف را برطرف کرده و برای تشخیص راستگویی و دروغگویی سوژه صرفاً به دادگان همان سوژه نیاز است.

کلیدواژه‌ها


عنوان مقاله [English]

Guilty Knowledge Test by Chaotic Processing of Electroencephalogram Signals Based on Fuzzy Recurrence Plot

نویسندگان [English]

  • Sakineh Razavi 1
  • Amin Janghorbani 2
  • Mohammad Bagher Khodabakhshi 3
1 Master's student, Faculty of Modern Sciences and Technologies, Semnan University, Semnan, Iran
2 Assistant Professor, Faculty of Modern Sciences and Technologies, Semnan University, Semnan, Iran
3 Assistant Professor, Faculty of Medical Engineering, Hamedan University of Technology, Hamedan, Iran
چکیده [English]

The EEG-based guilty knowledge test (GKT) is one of the most frequent lie detection methods. Recurrence plot analysis is a conventional chaotic signal processing method applied in different lie detection studies. One of the most important challenges of this method is selecting the appropriate threshold as the criterion of state recurrence in the phase space. Inappropriate selection of this threshold significantly affects the performance of this method. So in this study, the fuzzy recurrence plot is applied to overcome this challenge. This method is applied to transform EEG trials into grayscale texture images. Then, the gray-level co-occurrence matrix (GLCM) is used to extract the texture features from these images. Finally, The extracted features are classified using the K-NN classifier. The classification results of the 4-D feature vectors with 90% accuracy indicate the superiority of this method compared to the classic RQA method with 13-D feature vectors. This reduction in feature vector dimension improves the train and test speed and generalization of the KNN as a lazy learner. Moreover, the subject-based EEG-trial processing approach of this research eliminates the need for data set from various subjects and the only data set required to determine the sincerity of each subject is solely its own data set.

کلیدواژه‌ها [English]

  • Guilty Knowledge Test
  • Electroencephalogram
  • Chaotic Processing
  • Fuzzy Recurrence Plot
  • K-Nearest Neighbors

Smiley face

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دوره 10، شماره 2 - شماره پیاپی 38
شماره پیاپی 38، فصلنامه تابستان
مهر 1401
صفحه 87-104
  • تاریخ دریافت: 14 تیر 1400
  • تاریخ بازنگری: 19 اردیبهشت 1401
  • تاریخ پذیرش: 18 مرداد 1401
  • تاریخ انتشار: 01 مهر 1401