Functional brain network and multichannel analysis for the P300-based brain computer interface system of lying detection |
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| Affiliation: | 1. College of Communication Engineering, Jilin University, Changchun, Jilin 130012, China;2. School of Computing and Information Systems, Athabasca University, Athabasca, Alberta T9S 3A3, Canada;3. Zhuhai College of Jilin University, Zhuhai, Guangdong 519041, China;4. School of Information and Electrical Engineering, Ludong University, Yantai 264025, China;1. College of Mathematics Physics and Information Engineering, Jiaxing University, 56 Yuexiu Road (South), Jiaxing 314001, China;2. School of Computer Science and Information Technology, RMIT University, GPO Box 2476, Melbourne 3001 Victoria, Australia;3. Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China;1. Shanghai University of Finance and Economics, Shanghai 200433, China;2. Institut-Mines Télécom, Télécom SudParis, 9 rue Charles Fourier, Evry Cedex 91011, France;3. Universidad Carlos III de Madrid, Av de la Universidad, 30, Leganés 28911, Madrid, Spain;4. School of Earth Science and Engineering, Hohai University, Nanjing 210098, China;5. State Key Laboratory of Geo-information Engineering, Xi’an 710054, China;1. Department of Computer Science and Artificial Intelligence (DECSAI), University of Granada, C/ Daniel Saucedo Aranda, s/n, 18071 Granada, Spain;2. CEMSE Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia |
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| Abstract: | Deception is a complex cognition process which involves activities in different brain regions. However, most of the ERP based lie detection systems focus on the features of ERPs from few channels. In this study, we designed a multi-channel ERP based brain computer interface (BCI) system for lie detection. Based on this, two new EEG feature selection approaches, bootstrapped geometric difference (BGD) and network analysis were proposed and applied to feature recognition and classification system. Unlike other methods, our approaches focus on the changes of EEGs from different brain regions and the correlation between them. For the test, we focus on visual and auditory stimuli, two groups of subjects went through the test and their EEGs were recorded. For all subjects, BGD of the P300 for all the scalp electrodes combined with SVM classifier showed the average rate of recognition accuracy was 84.4% and 82.2% for visual and auditory modality respectively. Statistical analysis of network features indicated the difference in the two groups were significant and the average accuracy rate reached 88.7% and 83.5% respectively, and the guilty group showed more obvious small-world property than innocent group. The results suggest the BGD and network analysis based approaches combined with SVM are efficient for ERP based expert and intelligent system for detection and evaluation of deception. The combination of these methods and other feature selection approaches can promote the development and application of ERP based lie detection system. |
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