کنفرانس بین المللی میکروالکترونیک ایران

صفحه اصلی / هفتمین کنفرانس بین المللی میکروالکترونیک ایران

Robust EEG-Based Lie Detection via GCN and Type-2 Fuzzy Activation

نویسندگان :

Sobhan Sheykhivand¹ Tayebeh Azadmousavi² Nastaran Khaleghi³ Mehdi Zarei⁴

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.