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On the Use of Brainprints as

Passwords

Zhanpeng Jin

Department of Electrical and Computer Engineering

Department of Biomedical Engineering

Binghamton University, State University of New York (SUNY)

9/24/2015 2015 Global Identity Summit (GIS) 1

Outline

• Introduction

• Methods

• Supervised machine learning approach

• Similarity-based pattern matching approach

• Unsupervised feature learning approach

• Multi-stimulus, multi-channel fusion

• Datasets and Results

• Ongoing work

• Conclusions


Why Brainwaves?

• Existing biometric methods

• Unique physiological and behavior features to identify individuals

• E.g., fingerprint, palm, iris, face and voice

• Problems and limitations

• Duplicable and noncancelable

• Accidental and intentional disclosure

• Not safe enough for high security agencies

• Safety-threatening to the users


Recent Biometrics Breaches


Why Brainwaves?

• Electroencephalograph (EEG)

• Representing brain’s electrical activity by

measuring the voltage fluctuations on the

scalp surface

• Advantages

• Safety for the user, not only for the system

• Practical solution to duress

• Quantify the uniqueness of our cognition

• Non-volitional EEG brainwaves

• Unique memory and knowledge by the user

• Intuitive response not controlled by the user

Time-locked to what?


• Brain response to a stimulus

• Calculation

• Time-locked average

0 100 200 300 400 500 600 700 800 900 1000 1100-50

0

50Raw EEG segments (Sub:1, Ch:Oz, Sti:BW food)

0 100 200 300 400 500 600 700 800 900 1000 1100-50

0

50

am

plit

ude (

uV

)

0 100 200 300 400 500 600 700 800 900 1000 1100-40

-20

0

20

time (ms)

0 100 200 300 400 500 600 700 800 900 1000 1100

-15

-10

-5

0

5

10

15ERP of 36 trails (Ch:Oz, Sti:BW food)

time (ms)

ampl

itude

(uV

)

Sub 1

Sub 13

Sub 29

0 100 200 300 400 500 600 700 800 900 1000 1100-10

0

10

ERPs of Sub 1 (Ch:Oz, Sti:BW food, Trails:35)

ERP 1

ERP 2

0 100 200 300 400 500 600 700 800 900 1000 1100

-10

0

10

ERPs of Sub 13 (Ch:Oz, Sti:BW food, Trails:35)

am

plit

ude (

uV

)

0 100 200 300 400 500 600 700 800 900 1000 1100-5

0

5

10ERPs of Sub 29 (Ch:Oz, Sti:BW food, Trails:35)

time (ms)


Event-Related Potential (ERP)

• Feature extraction

• Wavelet package decomposition

• Subbands

• Delta: 0-4 Hz

• Theta: 4-8 Hz

• Alpha: 8-15 Hz

• Beta: 15-30 Hz

• Gamma: 30-60 Hz

• Features:

• Mean

• Standard deviation

• Entropy

• Neural network

• Hidden layer: 5-60 neurons


Supervised Learning Approach

Data Acquisition and Results

• Sampling • 500 Hz, 1.1 seconds

• Subjects • 32 adult participants: 11 females,

age range 18-25, mean age 19.12

• Channel • Oz

• Stimuli • Acronyms: e.g. MTV, TNT

• Presentation • 2

• ERP • 50 trails average


Pattern Similarity Approach

• Euclidean Distance (ED) • Measures the distance between two time series by aligning the n-th

point of one time series with the n-th point of the other one

• Dynamic Time Warping (DTW) • Finds the optimal alignment between two time series even they are out

of phase according to the time

Fast DTW


Data Acquisition

• Sampling

• 500 Hz, 1.1 seconds

• Subjects

• 30 adult participants: 14

females, age range 18-25,

mean age 19.53

• Channels

• Pz, O1, O2, O4

• Presentation

• 2

• ERP: 50 trails average

• Stimuli

• Words: e.g., BAG, FISH

• Pseudo words: e.g., MOG, TRAT

• Acronyms: e.g. MTV, TNT

• Illegal strings: e.g. BPW, PPS


Results

• Channel Oz shows stronger

distinguishing capability

• The occipital region seems

to be a best location to

reflect the brain response to

visual stimuli

• Brain responses are more

distinguishable to unfamiliar

or well understood stimuli

• Illegal strings and words

have higher accuracy than

acronyms and pseudo words

Channel

Stimuli Pz O1 O2 Oz

Acronyms 53.33% 58.17% 57.83% 67.83%

Illegal Strings 72.00% 71.17% 72.50% 81.17%

Words 68.67% 70.33% 70.17% 78.00%

Pseudo words 57.50% 61.83% 64.17% 68.83%

Channel

Stimuli Pz O1 O2 Oz

Acronyms 33.83% 45.67% 42.00% 55.67%

Illegal Strings 47.00% 43.67% 46.17% 67.17%

Words 49.33% 47.50% 49.17% 62.83%

Pseudo words 36.50% 43.50% 42.67% 49.33%

Results of ED

Results of fast DTW


• Sparse Autoencoder

• Set the outputs equal to the inputs

• Softmax Classifier

• Generalize logistic regression to classification problems

• Semi-supervised Learning

• Sparse Autoencoder + Softmax

𝐽𝑠𝑝𝑎𝑟𝑠𝑒 𝑊, 𝑏 = 𝐽 𝑊, 𝑏 + 𝛽 𝐾𝐿(𝜌||𝜌 𝑗)

𝑠2

𝑗=1

𝑝 𝑦 𝑖 = 𝑗 ℎ 𝑖 ; 𝜃 =𝑒𝜃𝑗

𝑇ℎ(𝑖)

𝑒𝜃𝑙𝑇ℎ(𝑖)𝑘

𝑙=1


Unsupervised Feature Learning

Convolutional Neural Network (CNN) • First proposed by LeCun in 1998, called LeNets*

+ + Softmax


Data Acquisition

• Sampling

• 500 Hz, 1.1 seconds

• Subjects

• 29 adult participants: 14

females, age range 18-43,

mean age 20.69

• Channels

• 30

• Presentation

• 1

• ERP

• 25 trails average

• Stimuli (8 categories): – BW text

– BW Gabor

– BW celeb

– color targets

– BW food

– color food

– hamburger

– passthought


BW Text

• GRE words

• 100 words

• Good results with previous

experiment

• Low frequency words

• Not everyone has meaning

for every subject


BW Gabor Patches

• 100 randomly generated


BW Celebrities and Foods

• Norming for most loved and hated

• 10 celebrities and foods chosen

• 10 items of each

• 100 celebrities

• 100 foods


Color Targets

• Press a button when you see color

75% 25%


BW Food

• 90 food items

Color Food


Results • Low accuracy @(BW gabor)

• High accuracy @(BW celebrities, BW food, and color food)

• Higher accuracy @(Occipital region)

• Accuracy: CNN > SL > CC


Results

• Majority Voting

• Improved the performance of accuracy


Multi-Channel, Multi-Stimulus Fusion


Different stimulus types likely tap into different functional

brain networks – semantic interpretation • Sine gratings: lateral occipital sites

• Color foods: broader region of more anterior scalp sites

• Celebrities: channels intermediate between sine grating and food

areas

Results


Full Combination:

• 6 stimulus types

• 30 channels

Slimmest Combination:

• 4 single-stimulus

classifiers (BW foods,

color foods, color

targets, BW celebrities)

• 1 channel (the middle

occipital (Oz))

Ongoing Work

• Psychological Coercion Attack

• Blackmail-type chronic coercion

• Threat-of-violence-type acute coercion

• Rationale: Forms of coercion that place psychological stress on the

user may cause brain activity to deflect.

• Psychological Entrainment Attack


Conclusions

• Brainprints are a promising and compelling biometric,

particularly for high security scenarios.

• Rooted in unique non-volitional brain responses, associated

with unique memory and knowledge base.

• Cancelable through brainprint recalibrations using different

types of stimulus

• Accurate among individuals and stable over time

• Resistance to coercion, entrainment, and other psychological

attacks

• Challenges in brainwave acquisition and emotional status.


Questions?

9/24/2015 2015 Global Identity Summit (GIS)

Thanks for Listening

More Information

[email protected]

26

This research is supported by NSF and SUNY.