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Brain Wave Based Authentication Kennet Fladby 2008.

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Presentation on theme: "Brain Wave Based Authentication Kennet Fladby 2008."— Presentation transcript:

1 Brain Wave Based Authentication Kennet Fladby 2008

2 Outline 1. Introduction 2. Research questions 3. Experimental work 4. Results 5. Conclusion 6. Further work

3 1-1. Brain waves The brain contains about 100 billion neurons. Neurons generates and leads electrical signals. The sum of these electrical signals generates an electric field. Fluctuations in the electric field can be measured. Electroencephalographic (EEG)

4 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 TaskDescription RelaxRelax in a normal fashion ColorVisualize the red color RotateMentally rotate a house PasswordThink about the password ’BrainWaveS’ MusicThink about a melody/song WordsGenerate words with capital letter ’M’ CountCount upwards starting from 1 ReadRead a random provided text

8 3-2. Setup 10 participants – 3 sessions, 3 recordings of each task per session – Each recording lasts 20 seconds (2560 samples) – Eyes closed Number of recordings – 72 per participant ( 24 minutes ) – 720 total (4 hours )

9 3-3. Physical movement anomalies

10 3-4. Initialization problem

11 3-5. Frequency domain The brain operates at low frequencies usually divided into six frequency bands: Frequency bandRange Delta1 – 4Hz Theta4 – 8Hz Alpha8 – 12Hz Beta-Low12 – 20Hz Beta-High20 – 30Hz Gamma30 – 50Hz

12 3-6. Fast fourier transform

13 3-7. Feature extraction Time domain features – Mean sample value – Zero crossing rate – Values above zero Frequency domain features – Peak frequency – Peak frequency magnitude – Signal power in each frequency band Pdelta, Ptheta, Palpha, PbetaLow, PbetaHigh, Pgamma – Mean band power – Mean phase angle

14 3-8. Statistics Chi-square goodness-of-fit test – Samples and features do not follow normal distribution. Correlation – High correlation between PbetaLow and PbetaHigh (8 out 10 participants).

15 3-9. Distance metric 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. Distance computation 1 Computation: All vs All Genuine attempts: – d(signal1,signal2) from the same participant Fraudulent attempts – d(signal1,signal2) from different participants Requirement: – d(signal1,signal2) must be from the same task

17 4-2. DET-Curve 1 EER = 30.28%

18 4-3. Distance computation 2 Computation: All vs All Genuine attempts: – d(signal1, signal2) from the same participant Fraudulent attempts – d(signal1, signal2) from different participants 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 Task with the best average distance ParticipantSession 1Session 2Session 3 1ColorCountWords 2Count Password 3ColorCountRotate 4WordsColorRotate 5 Password 6Count Words 7RotateWordsColor 8RotatePasswordWords 9 Music 10RelaxColorRotate

21 4-6. Distance computation 3 Computation: Task selection Genuine attempts – d(signal1,signal2) from the same participant Fraudulent attempts – d(signal1,signal2) from different participants Requirement – d(signal1,signal2) must be from the selected session 1 task.

22 4-7. DET-Curve 3 EER = 21.46%

23 4-8. Distance computation 4 Computation: Task selection Genuine attempts – d(signal1,signal2) from the same participant Fraudulent attempts – d(signal1,signal2) from different participants Requirement – d(signal1,signal2) must be from the selected session 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 Similiarities are session based – Two consequtive signals are very similar Equipment dependant – Signal gets better over time – Captures too much physical movement One sensor is not enough – Limited information – Low sample rate

27 6. Further work Better distance metric – Identify more feature relations – Try different feature combinations Better selection of tasks – Tasks designed for the Fp1 location New equipment – Better filtering – Increased sample frequency – More sensors – Different sensor locations

28 Thank you for listening! Questions ?


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