Constructing the lie detection system with fuzzy reasoning approach

Ying Fang Lai, Mu Yen Chen, Hsiu Sen Chiang

研究成果: Article同行評審

21 引文 斯高帕斯(Scopus)


Current approaches to lie detection generally rely on specialized instrumentation or environmental conditions which can be time- and cost-intensive to secure and produce questionable results. This study uses electroencephalographic (EEG) variability and fuzzy theory to develop a lie detection model and rule set, identifying sensitive and useful EEG frequency bands to accurately measure lying states based on spectral analysis. Twenty subjects participated in card tests accompanied by EEG recording to evaluate the performance of the proposed model against other data mining methods. The result shows that our proposed model has a lie detection accuracy rate of 89.5% and compares well with other data mining methods. A mobile prototype for real-time lie detection is developed by integrating commercial brainwave measurement instruments with mobile devices. The proposed device can facilitate real-time and accurate polygraphy, while reducing the disadvantages of conventional lie detection approaches.

頁(從 - 到)169-176
期刊Granular Computing
出版狀態Published - 2018 六月

All Science Journal Classification (ASJC) codes

  • 人工智慧
  • 電腦科學應用
  • 資訊系統


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