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An efficient Concealed Information Test: EEG feature extraction and ensemble classification for lie identification

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Abstract

EEG-based lie detectors have become popular over polygraphs because it cannot be controlled by human intentions. Various studies have performed “Guilty Knowledge Test” or “Concealed Information Test” by creating a mock crime scenario to identify changes in brain potential. In this study, an individual’s behavior during lying is analyzed and a new scenario is developed for “Concealed Information Test.” This work involves a mock crime scenario using an EEG acquisition device for 10 participants. Data acquisition has been performed by placing 16 electrodes on the subjects’ scalp. For this experiment, the subject has to recognize faces of some known and unknown personalities among 10 images flashed. These images behave as stimulus for the subject which generate corresponding brain responses. Various feature extraction approaches such as statistical, time domain, frequency domain and time–frequency domain are applied to the 16- channel EEG data. For classifying a subject as guilty or innocent, five classifiers have been applied on subject-wise EEG data. Moreover, the classifiers’ ranking is considered based on the performance of classifiers. An ensemble framework is developed by aggregating the results of the best three classifiers out of the tested five classifiers. The classifiers’ results are aggregated using a weighted voting approach and have been compared with popular conventional approaches using various classification performance measures. Results present a comparative performance of different feature extraction approaches and classifiers using subject-wise single-trial EEG data. The wavelet approach performs better for EEG data of most of the subjects. A comparison between base classifiers and ensemble framework is provided with the ensemble approach outperforming over the base classifiers. Further proposed framework is compared with some existing approaches, and a highest accuracy of 92.4% has been achieved.

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Acknowledgements

The authors would like to thank all the participants who are involved in our study and provided us CIT data.

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Authors and Affiliations

  1. National Institute of Technology, Goa, Ponda, India

    Annushree Bablani, Damodar Reddy Edla & Venkatanareshbabu Kuppili

  2. Madanapalle Institute of Technology and Science, Madanapalle, A.P., India

    Diwakar Tripathi

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  1. Annushree Bablani
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  2. Damodar Reddy Edla
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  3. Diwakar Tripathi
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  4. Venkatanareshbabu Kuppili
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Correspondence to Annushree Bablani.

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Bablani, A., Edla, D.R., Tripathi, D. et al. An efficient Concealed Information Test: EEG feature extraction and ensemble classification for lie identification. Machine Vision and Applications 30, 813–832 (2019). https://doi.org/10.1007/s00138-018-0950-y

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