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 Task Description Relax Relax in a normal fashion Color
Visualize the red color
Rotate
Mentally rotate a house
Password
Think about the password ’BrainWaveS’
Music
Think about a melody/song
Words
Generate words with capital letter ’M’
Count
Count upwards starting from 1
Read
Read a random provided text

8. 3-2. Setup 10 participants Number of recordings
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 band
Range
Delta
1 – 4Hz
Theta
4 – 8Hz
Alpha
8 – 12Hz
Beta-Low
12 – 20Hz
Beta-High
20 – 30Hz
Gamma
30 – 50Hz

12. 3-6. Fast fourier transform

13. 3-7. Feature extraction Time domain features Frequency 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 Correlation
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. Task with the best average distance
4-5. Task selection
Task with the best average distance
Participant
Session 1
Session 2
Session 3 1
Color
Count
Words 2
Password 3
Rotate 4 5 6 7 8 9
Music 10
Relax

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%

26. 5. Conclusion Similiarities are session based Equipment dependant
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 Better selection of tasks
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?