2. Project Final Presentation Enhanced Lie Detector By: Lander Shiran and Balaban Nir Supervisor: Lange Danny

3. Project Goals Characterizing signals from the human body, and decides whether the person told the truth. The project is an extension of the regular lie detector but it uses other methods for decision.

4. Description Comparing signals from both sides. The right hand indicates the arousal of the left hemisphere and the left hand, the right hemisphere.

5. Theoretical Overview – Left and Right Brain Split The brain is divided into two parts: the left and the right. In the left part of the brain, logical, verbal and sequential thoughts are processed. The right part is responsible for emotional, non-verbal thoughts and feelings.

6. Theoretical Overview TruthFalse Both lobes are aroused similarly. The right and the left lobes are working together synchronically. The left lobe is aroused and the right is suppressed. The right lobe is closer to reality and only the left brain can make up new facts – lie.

7. The ECG Machine The ECG machine monitors the signal from the subject body to digital signals in the computer, there we can process them as we find fit. The machine have number of detectors that should be attached to the body in the right places.

8. The Questionnaire – Calibration Phase In order to recognize a lie, we must first characterize the truth. That is why the calibration phase exists: we ask the subject 10 questions and get 10 true answers. The lie will deviate from the truth “ section ”.

9. The Questionnaire – Examination Phase 1. The subject will write down the true answers and keep them for himself. 2. The subject will answer the investigators, while monitored by the ECG machine and will try to “ fool ” it by telling lies. 3. The subject will give the prewritten answers to the investigators.

10. The Questionnaire – Goals 1. Because the subject writes down the answers, he cannot regret  more pressure. 2. We use the practice of “ the carrot and the stick ” : “ anybody who will fool the machine will get a pizza ”  a motive. 3. We finally get a database of signals that can be organized by true & false.

11. Measurements – Electrodes Positions The ideal scenario: We would like two clean signals from both sides of the body – 2 common are needed. Common Channel

12. Measurements – Electrodes Positions The symmetric scenario: The common will be placed in the middle of the body and will create symmetric signals. Common Channel

13. Measurements – Electrodes Positions The a - symmetric scenario: The common will be placed on one of the hands so we ’ ll get one noisy channel and one clean channel. Common Channel

14. Measurements – The Signals The a - symmetric scenario: At each question we will wait for 15 sec before presenting the question so that the subject relaxes. 15 sec

15. Preprocessing The Signals 1. Because we have one clean signal and one noisy signal we must filter them in order to compare them. The ECG signal is of greater frequency from the data and therefore we use LPF on both signals (the same delay). 2. Some signals were taken out of the database because the subject was not aroused as expected.

16. Preprocessing The Signals 3. Taking only the appropriate interval, from the 15 sec until the signal fades. In order to achieve that a designated GUI was created:

17. Preprocessing The Signals LP Filter 8 sec

18. Analyzing The Signals - Parameters

19. 2. Energy difference - in order to find the dominant hemisphere the difference between the two hemispheres energies is calculated:

20. Analyzing The Signals - Parameters 3. Correlation – assuming that when a subject tells the truth, his two hemispheres work respectively and when a subject lies, the harmony ceases. 4. Delay between peaks – according to the article, there may be a delay between the reaction of the hemispheres. The delay was calculated according to the peaks.

21. Analyzing The Signals – Parameters Examples We use two methods for analyzing the parameters: 1. Parameter vs. Question number :

22. Analyzing The Signals – Parameters Examples We use two methods for analyzing the parameters: 2. Parameter 1 vs. Parameter 2 :

23. Analyzing The Signals – Pattern Recognition PCA – Principal Component Analyze PCA involves a mathematical procedure that transforms a number of correlated variables into a number of uncorrelated variables (not necessarily what we want) called principal components. The first principal component accounts for as much of the variability in the data as possible, and each succeeding component accounts for as much of the remaining variability as possible.

24. Analyzing The Signals – Pattern Recognition PCA – Principal Component Analyze PCA

25. Analyzing The Signals – Pattern Recognition Questionnaire

26. Analyzing The Signals – Pattern Recognition Do you like beer? Yes! True PCA Questionnaire

27. Analyzing The Signals – Pattern Recognition Do you think you need a diet? Nope False PCA Questionnaire

28. Analyzing The Signals – Pattern Recognition PCA Results correlated signals non-correlated signals

29. Analyzing The Signals – Pattern Recognition The first order vector is not expected to give us much information, but we hope the second or the third order will help us to distinguish between lie and truth. The PCA algorithm was executed 4 times – True left, True right, False left and False right.

30. Analyzing The Signals – Pattern Recognition 1 23

31. Second order

32. Analyzing The Signals – Conclusions As we can see, the second order of the PCA can identify lies: in truth, the signals are corresponding to each other while in lie, the signal oppose each other.

33. THE END