Brain Wave Based Authentication 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. 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)

1-2. 10-20 System

1-3. EEG signal: 20 seconds, 128Hz

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?

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

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 )

3-3. Physical movement anomalies

3-4. Initialization problem

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

3-6. Fast fourier transform

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

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).

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

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

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

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.

4-4. DET-Curve 2 EER = 23.26%

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

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.

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

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.

4-9. DET-Curve 4 EER = 17.08%

4-10. DET-Curve 1-4 EER = 30.28% EER = 23.26% EER = 21.46%

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

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

Thank you for listening! Questions?