A Deep Learning Model for EEG-Based Lie Detection Test Using Spatial and Temporal Aspects

Abstract

Lie detection test is highly significant task due to its impact on criminology and society. Computerized lie detection test model using electroencephalogram (EEG) signals is studied in literature. In this paper we studied deep learning framework in lie detection test paradigm. First, we apply a preprocessing technique to utilize only a small fragment of the EEG image instead of the whole image. Our model describes a temporal feature map of the EEG signals measured during the lie detection test. A deep learning attention model (V-TAM) extracts the temporal map vector during the learning process. This technique reduces computational time and lessens the overfitting in Deep Learning architectures. We propose a Cascading attention model with a deep learning convolutional neural network (CNN). V-TAM model extracts local features and global features in separate paths spatial and temporal. Also, to enhance the EEG segmentation precision, a novel Visual-Temporal Attention Model (V-TAM) is proposed. The accuracy was evaluated using data measured from a sensor from a public dataset of 9512 subjects during fifteen minutes lie detection task. We compared our model with three recent published models. Our proposed model attained the highest performance of (98.5%) with (p < 0.01). The visual-temporal model of the proposed platform shows an optimized balance between prediction accuracy and time efficiency. Validation investigation were performed to prove the correctness and reliability of the proposed method through sizing of the input data, proving its effectiveness in attaining satisfactory performance by using only a smaller size input data.