Identifying Concealed Information Using Wavelet Feature Extraction and Support Vector Machine

In this paper, a new approach based on wavelet feature extraction and support vector machine (SVM) is proposed to identify concealed information. Firstly, the wavelet coefficients of event related potential (ERP) in delta, theta, alpha and beta bands are extracted as useful features of brain activity responded to different stimulus information. Next, a Fisher discriminant criterion is applied to reduce the feature vector dimensions. Finally, a SVM classifier is employed to classify the data and the leave-one-out cross validation method is used for accuracy assessment. For the evaluation of the method, 16 subjects went through the designed CIT paradigm and their respective brain signals were recorded. The experimental results show that SVM classifier can effectively differentiate between concealed information and irrelevant information, and it achieves the maximum classification accuracy of 90.63%. The investigation also suggests that the wavelet decomposition coefficient can reflect more comprehensive time-frequency information correlating with deception, which can effectively distinguish concealed information between irrelevant information.