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Brain W ave B ased A uthentication

Brain W ave B ased A uthentication. Kennet Fladby 2008. Outline. 1. Introduction 2. Research questions 3. Experimental work 4. Results 5. Conclusion 6. Further work. 1-1. Brain waves. The brain contains about 100 billion neurons.

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Brain W ave B ased A uthentication

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  1. BrainWaveBasedAuthentication

    Kennet Fladby 2008
  2. Outline 1. Introduction 2. Research questions 3. Experimentalwork 4. Results 5. Conclusion 6. Furtherwork
  3. 1-1. Brainwaves The braincontainsabout 100 billion neurons. Neurons generates and leads electrical signals. The sum oftheseelectrical signals generates an electricfield. Fluctuations in theelectricfieldcan be measured. Electroencephalographic (EEG)
  4. 1-2. 10-20 System
  5. 1-3. EEG signal: 20 seconds, 128Hz
  6. 2. Research questions Is it possible to authenticate by means of brain waves with only one EEG sensor? What feature should be extracted from the signals? Do we have to authenticate based on a person’s thoughts or can we use the brain waves as a biometric directly? Will a distance metric approach work? What is the best FMR and FNMR we can achieve?
  7. 3-1. Tasks
  8. 3-2. Setup 10 participants 3 sessions, 3 recordingsofeachtask per session Eachrecording lasts 20 seconds (2560 samples) Eyes closed Numberofrecordings 72 per participant ( 24 minutes ) 720 total (4 hours )
  9. 3-3. Physicalmovementanomalies
  10. 3-4. Initialization problem
  11. 3-5. Frequencydomain The brain operates at low frequencies usually divided into six frequency bands:
  12. 3-6. Fast fouriertransform
  13. 3-7. Feature extraction Time domainfeatures Meansamplevalue Zero crossing rate Valuesabove zero Frequencydomainfeatures Peakfrequency Peakfrequency magnitude Signal power in eachfrequency band Pdelta, Ptheta, Palpha, PbetaLow, PbetaHigh, Pgamma Mean band power Meanphase angle
  14. 3-8. Statistics Chi-squaregoodness-of-fittest Samples and features do not follow normal distribution. Correlation HighcorrelationbetweenPbetaLow and PbetaHigh(8 out 10 participants).
  15. 3-9. Distancemetric d = d(signal1,signal2) : X = signal1 Y = signal2 d1 = |X.PbetaLow / X.PbetaHigh - Y.PbetaLow / Y.PbetaHigh| d2 = |X.PbetaLow / Y.PbetaLow - Y.PbetaHigh /X.PbetaHigh| d3 = |X.Palpha / X.PbetaLow - Y.Palpha / Y.PbetaLow| d4 = |X.Palpha/ Y.Palpha - Y.PbetaLow / X.PbetaLow| d= d1 + d2 + d3 + d4
  16. 4-1. Distancecomputation 1 Computation: All vs All Genuine attempts: d(signal1,signal2) from the same participant Fraudulent attempts d(signal1,signal2) from differentparticipants Requirement: d(signal1,signal2)must be from the same task
  17. 4-2. DET-Curve 1 EER = 30.28%
  18. 4-3. Distancecomputation 2 Computation: All vs All Genuine attempts: d(signal1, signal2) from the same participant Fraudulent attempts d(signal1, signal2) from differentparticipants Requirement: d(signal1, signal2) must be from the same task AND the same session.
  19. 4-4. DET-Curve 2 EER = 23.26%
  20. 4-5. Task selection
  21. 4-6. Distancecomputation 3 Computation: Task selection Genuine attempts d(signal1,signal2) from the same participant Fraudulent attempts d(signal1,signal2) from differentparticipants Requirement d(signal1,signal2) must be from theselectedsession 1 task.
  22. 4-7. DET-Curve 3 EER = 21.46%
  23. 4-8. Distancecomputation 4 Computation: Task selection Genuine attempts d(signal1,signal2) from the same participant Fraudulent attempts d(signal1,signal2) from differentparticipants Requirement d(signal1,signal2) must be from theselectedsession 1 task AND the same session.
  24. 4-9. DET-Curve 4 EER = 17.08%
  25. 4-10. DET-Curve 1-4 EER = 30.28% EER = 23.26% EER = 21.46% EER = 17.08%
  26. 5. Conclusion Similiaritiesaresessionbased Two consequtive signals areverysimilar Equipmentdependant Signal getsbetter over time Capturestoomuchphysicalmovement One sensor is not enough Limited information Lowsample rate
  27. 6. Furtherwork Better distancemetric Identify more feature relations Trydifferent feature combinations Better selectionoftasks Tasks designed for the Fp1 location New equipment Better filtering Increasedsamplefrequency More sensors Different sensor locations
  28. Thankyoufor listening! Questions?
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