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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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