RETRACTED ARTICLE: PSO-based optimization forEEG data and SVM for efficient deceit identification

• Published in: Soft computing (Berlin, Germany) Vol. 27; no. 14; pp. 9835 - 9843
• Main Authors: Boddu, Vijayasree, Kodali, Prakash
• Format: Journal Article
• Language: English
• Published: Heidelberg Springer Nature B.V 01.07.2023
• Subjects: Algorithms; Feature selection; Optimization; Parameter identification; Parameter sensitivity; Particle swarm optimization; Performance enhancement; Random variables; Support vector machines; Velocity; Wavelet transforms
• ISSN: 1432-7643; 1433-7479
• DOI: 10.1007/s00500-023-08476-3

Deception is a well-known term that involves acting in a way that it leads another person to believe something that is not true. Deception is a very common occurrence in daily life, and many times it becomes a big problem for national security. To cope with this problem, deception detection has gained a lot of attention recently. In this paper, we have tried to come up with a deceit identification system where we have used electroencephalograph (EEG) data collected by performing a concealed information test. To improve the performance of the system, first we have tried to select the optimal subset of the EEG channels using binary particle swarm optimization and secondly performed support vector machine hyper-parameter optimization using continuous version of PSO. The proposed model is validated using EEG dataset. The performance of the proposed system has resulted in increase of accuracy from 76.98 to 96.45% which is a significant improvement. Also, the proposed approach outperformed in terms of sensitivity, specificity, F1-score and G-measure when compared with state-of-the-art models.