عنوان	Robust EEG-Based Lie Detection via GCN and Type-2 Fuzzy Activation
نوع پژوهش	مقاله ارائه شده
کلیدواژه‌ها	EEG, deep learning, graph convolutional network, lie detection
چکیده	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.
پژوهشگران	سبحان شیخی وند کشتیبان (نفر اول)، طیبه آزاد موسوی (نفر دوم)، نسترن خالقی (نفر سوم)، مهدی زارعی (نفر چهارم)