Functional brain network and multichannel analysis for the P300-based brain computer interface system of lying detection

• Successful use of wavelet packet analysis to extract P300 from ERP.
• We propose two new methods for ERP feature extraction and classification.
• Bootstrapped geometric difference analysis is useful for lie detection.
• Functional network analysis is first used in lie detection system.
• Functional brain network in guilty group shows enhanced small world property.

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.