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
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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
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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?
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• 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)
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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
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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
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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
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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
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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
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• 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
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Unsupervised Feature Learning
Convolutional Neural Network (CNN) • First proposed by LeCun in 1998, called LeNets*
+ + Softmax
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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
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BW Text
• GRE words
• 100 words
• Good results with previous
experiment
• Low frequency words
• Not everyone has meaning
for every subject
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BW Celebrities and Foods
• Norming for most loved and hated
• 10 celebrities and foods chosen
• 10 items of each
• 100 celebrities
• 100 foods
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Color Targets
• Press a button when you see color
75% 25%
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Results • Low accuracy @(BW gabor)
• High accuracy @(BW celebrities, BW food, and color food)
• Higher accuracy @(Occipital region)
• Accuracy: CNN > SL > CC
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Results
• Majority Voting
• Improved the performance of accuracy
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Multi-Channel, Multi-Stimulus Fusion
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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
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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
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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.
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Questions?
9/24/2015 2015 Global Identity Summit (GIS)
Thanks for Listening
More Information
26
This research is supported by NSF and SUNY.
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