Lying is a common social behavior, and accurate lie detection is crucial in areas such as national security. However, existing lie detection techniques have certain limitations. Therefore, more accurate and reliable tools and methods are needed to meet the practical needs of lie detection. In this context, this study discovered the potential value of electromyography (EMG) as a lie detection indicator. Specifically, this study used EMG for statistical analysis and machine learning recognition analysis of the lying process in an interactive scenario of active lying. Furthermore, we compared the performance of two traditional machine learning models and one deep learning model for lie detection based on EMG signals. In particular, time-dimensional and time-frequency-dimensional EMG features were used to mine and lie related features. Statistical results showed that compared to truth-telling, people tend to suppress their facial expressions when preparing to lie. Some facial muscle movements that were not be successfully suppressed after lying may be crucial for detecting lies. Moreover, our study offers theoretical hypotheses for the occurrence of micro-expressions and the feature of upper-lower facial asymmetry. Besides the statistic analysis, the analysis results of machine learning also demonstrated demonstrate the potential of machine learning models for EMG-based intelligent lying process analysis, particularly the RUSBoosted tree. In addition, our experiment result also proved that focusing on specific facial muscles, such as Corrugator supercilii, could improve the accuracy and efficiency of intelligent algorithms. In summary, our research results provide more insights into the cognitive and facial muscle movement patterns involved in lying based on statistical analysis and machine learning.