Research on Psychological Testing Methods of Criminal Suspects Based on Multi-features of EEG

P300 is a commonly used indicator for testing the psychology of criminal suspects, but it has problems such as weak signal and large amount of processed data. Aiming at such problems, based on experiments to simulate real case data, a psychological test method for criminal suspects based on multi-feature extraction of EEG signals in time domain, frequency domain, and time-frequency domain was proposed. In order to achieve psychological testing of criminal suspects in public security investigations, used the existing data to test and adjusted the model. In the time domain, the signal-to-noise ratio was improved by superimposing and averaging, and P300 was extracted. The amplitude and latency of the components were taken as the time domain features. In the frequency domain, the relationship between the EEG power and frequency reflected by the power spectrum estimation was used as the frequency domain features. In the time and frequency domain, the wavelet approximation coefficients of the corresponding frequency band extracted by the Mallat algorithm was used as the frequency domain features. Time-frequency domain features were selected through F-score. Finally, SVM was used as the classifier. The optimal penalty factor and kernel function were selected through cross-validation and dynamic grid. The results show that the method of multi-feature extraction can reflect the essential characteristics of the suspect’s EEG signal, reduce the amount of data processing, and have a higher classification accuracy.