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صفحه اصلی
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هفتمین کنفرانس بین المللی میکروالکترونیک ایران
Robust EEG-Based Lie Detection via GCN and Type-2 Fuzzy Activation
نویسندگان :
Sobhan Sheykhivand
1
Tayebeh Azadmousavi
2
Nastaran Khaleghi
3
Mehdi Zarei
4
1- University of Bonab
2- University of Bonab
3- University of Tabriz
4- Islamic Azad University, Hamedan Branch
کلمات کلیدی :
EEG،LIE DETECTION،DEEP LEARNING
چکیده :
Abstract—Lie detection has been widely used by governmental and non-governmental organizations to ensure the reliability of criminal confessions. Conventional polygraph devices, however, suffer from limitations and inconsistent accuracy. This paper presents a novel approach for lie detection using electroencephalogram (EEG) signals. An EEG dataset was collected from 20 participants, and a six-layer graph convolutional network (GCN) integrated with type-2 fuzzy sets was employed for feature selection and automatic classification. Experimental results demonstrate that the proposed method achieves over 90% accuracy even in noisy environments (SNR = 0 dB), outperforming existing techniques and showing strong potential for practical applications.
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بیشتر
ثمین همایش، سامانه مدیریت کنفرانس ها و جشنواره ها - نگارش 44.5.0
