这项研究引入了基于脑电图(EEG)的数据集来分析测谎。可以使用EEG信号执行各种分析或检测。使用EEG数据的测谎最近已经成为一个重要的话题。生活的方方面面,人们发现有必要互相说谎。虽然每天讲的谎言可能不会产生重大的社会影响,测谎在法律上变得至关重要,安全,求职面试,或可能影响社区的情况。本研究旨在获取用于测谎的脑电信号,创建一个数据集,并使用信号处理技术和深度学习方法对该数据集进行分析。EEG信号是使用具有5个通道的称为EmotivInsight的可穿戴EEG设备(AF3,T7,Pz,T8,AF4).每个人都参加了两个试验:一个是诚实的,另一个是欺骗的。每次实验,参与者评估了他们在实验前看到的珠子,并在视频剪辑前偷走了珠子。本研究分为四个阶段。在第一阶段,使用这些实验期间获得的EEG数据创建了LieWaves数据集.在第二阶段,进行了预处理。在这个阶段,应用自动和可调伪影去除(ATAR)算法从EEG信号中去除伪影。稍后,重叠滑动窗口(OSW)方法用于数据扩充.第三阶段,进行特征提取。为了实现这一点,通过结合离散小波变换(DWT)和包括统计方法(SM)的快速傅里叶变换(FFT)来分析脑电信号。在最后阶段,每个获得的特征向量使用卷积神经网络(CNN)分别分类,长短期记忆(LSTM)和CNNLSTM混合算法。在研究的结论,最准确的结果,达到99.88%的准确率,是使用LSTM和DWT技术生产的。通过这项研究,一个新的数据集被引入到文献中,它的目的是用这个数据集消除这个领域的不足。从数据集获得的评估结果表明,该数据集在该领域是有效的。
This study introduces an electroencephalography (EEG)-based dataset to analyze lie detection. Various analyses or detections can be performed using EEG signals. Lie detection using EEG data has recently become a significant topic. In every aspect of life, people find the need to tell lies to each other. While lies told daily may not have significant societal impacts, lie detection becomes crucial in legal, security, job interviews, or situations that could affect the community. This study aims to obtain EEG signals for lie detection, create a dataset, and analyze this dataset using signal processing techniques and deep learning methods. EEG signals were acquired from 27 individuals using a wearable EEG device called Emotiv Insight with 5 channels (AF3, T7, Pz, T8, AF4). Each person took part in two trials: one where they were honest and another where they were deceitful. During each experiment, participants evaluated beads they saw before the experiment and stole from them in front of a video clip. This study consisted of four stages. In the first stage, the LieWaves dataset was created with the EEG data obtained during these experiments. In the second stage, preprocessing was carried out. In this stage, the automatic and tunable artifact removal (ATAR) algorithm was applied to remove the artifacts from the EEG signals. Later, the overlapping sliding window (OSW) method was used for data augmentation. In the third stage, feature extraction was performed. To achieve this, EEG signals were analyzed by combining discrete wavelet transform (DWT) and fast Fourier transform (FFT) including statistical methods (SM). In the last stage, each obtained feature vector was classified separately using Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and CNNLSTM hybrid algorithms. At the study\'s conclusion, the most accurate result, achieving a 99.88% accuracy score, was produced using the LSTM and DWT techniques. With this study, a new data set was introduced to the literature, and it was aimed to eliminate the deficiencies in this field with this data set. Evaluation results obtained from the data set have shown that this data set can be effective in this field.