EEG Analysis of Familiar and Unfamiliar Objects Using Wavelet Energy and Shannon Entropy

Deception detection plays an important role in detecting fraud, especially in the context of security, criminal investigations, and social situations. Currently, Electroencephalogram (EEG)--based deception detection systems continue to be developed to measure the brain's electrical activity and discover unique brain wave signal patterns compared to polygraphs that can be fooled. This research will focus on finding patterns of differences in recognition of familiar and unfamiliar objects among the 30 respondents involved. The EEG channels were T3, T4, T5, T6, 01, and O2. The six channels will then be analyzed in the alpha, beta, and gamma sub-band after the band decomposition process using Discrete Wavelet Transform (DWT). The DWT value of each sub-band will then be feature-extracted using energy and Shannon entropy. The feature extraction results show that the unfamiliar condition is always higher in feature energy and Shannon entropy in all sub-bands and all channels. On the average the difference energy value between unfamiliar and familiar was 43%, while in Shannon entropy value the difference was 10,7%. This finding is sufficient to show the different EEG patterns between familiar and unfamiliar to be used in developing deception detection system.