Background: Bioengineering - Electronics - Signal processing. Biometry - Person Identification from brain wave detection University of «Roma TRE»

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1 Background: Bioengineering - Electronics - Signal processing Neuroscience - Source imaging - Brain functional connectivity University of Rome «Sapienza» BCI - Feature extraction from P300 brain potential (higher statistics) University of Rome «Sapienza» Biometry - Person Identification from brain wave detection University of «Roma TRE»

2 GOAL: Brain electrical activity for automatic user recognition Neuron cells EEG as a biometrics: Why? EEG carries genetic information and personality correlates (Vogel 70) Advantages EEG secret by its nature, acquisition sensor impossible to spoof Liveness detection by default User cannot be forced to donate it if under threat Universality Good temporal resolution allows continuous recognition Disadvantages Acquisition process

3 MAIN ISSUES Few data Lack of public longitudinal databases Single session recordings provide few samples for each class Inconvenience of the acquisission process Acquisition system: Electrodes Metal disks 3mm in diameter (usually silver or gold) Placed on the scalp International standards Cap Conductive gel (impedance 3-5kOhm) Amplifier and interface Collects the signals from the electrodes set Very sensitive and high quality

4 STATE OF THE ART Neurophysiology BCI/Cognitive Biometrics Anatomical and functional inter-individual differences - Genetic and environment influences (Zietsch et al. 2007) - personality correlates (Knyazev et al. 2010) Results - Significant genetic influence for PSD α band - Significant effect of E on antero-posterior spectral power gradient (APSPG) in specific bands Protocols and datasets from BCI Results Correct recognition rate (CRR) Genuine acceptance rate (GAR) False rejection rate (FRR) Half total error rate (HTER = GAR+FRR ) 2 Equal error rate (EER)

5 STATE OF THE ART Paper N Performance Poulos et al GAR=80-100% CRR=80-95% La Rocca et al CRR=100% Marcel et al HTER=8,1-12,3% He and Wang 10 7 HTER=4,1% Abdullah et al CRR=97% Das et al CRR=94% Su et al CRR=97,5% Paranjape et al GAR=49-82% La Rocca et al CRR=98,73% Campisi et al CRR=96,98% Riera et al EER=3,4% Palaniappan et al GAR=98,12% Brigham and Kumar GAR=98,96

6 Noise: MAIN ISSUES Original-blink Data pruned by ICA 1) Physiological/endogenous: Eye movement/blink Muscle (EMG) Movement Cardiogenic Sweat EMG Heartbeat

7 Noise: MAIN ISSUES 2) Extraphysiological/exogenous: externally generated Power supply Instrumentation interference poor electrode contact Electrode/lead movement Electrode spontaneous discharges It can be removed through a band-pass filter

8 MAIN ISSUES + Variability among individuals (inter-class) Feature space Fisher Discriminant analysis: Fisherbrains (Das et al. 2009) FLD space x 1 J w = wt S B w w T S W w ξ 1 S W 1 S B eig w ξ = w x x 2 Variability along time (intra-class) Feature space FLD space Class averages ξ 2 x 1 ξ 1 ξ 1 x 2 ξ 2 ξ 2

9 PRE-PROCESSING Downsampling (128 Hz) Filtering Spectral: brain rhythms Segmentation into frames (overlapped if necessary) t CAR Spatial: CAR V Ch i [ n] V Ch i [ n] 1 C T C T j1 V j i [ n] Detrend Mean removal Subtraction of the best-fit line (in the least-squares sense)

10 FEATURE ENGINEERING - SP AR modelling k k n w k n x a n x 1, ) ( ) ( ) ( m m q m R a m R q x q x, 0, ], [ ] [ ] [ 1 2, Yule-Walker equations ) (1 1, 1,, , 1,, q q q K q a K a a Levinson s recursion K = R x + R x [q] a 1, q 1 q=1 /σ 2 1

11 FEATURE ENGINEERING - Results (La Rocca et al. BIOSIGNALS 2013)

12 COH - CRR (%) Spectral features FEATURE ENGINEERING - SP (Palaniappan 2004) - Spectral power ratio for ERPs SPR = SP f /SP CRR=99,06% - PSD estimate Welch s modified periodogram EO EC - Spectral coherence: connectivity patterns F F COH i,j f = S i,j f 2 S i,i f 2 S j,j f 2 C PO C PO F C PO F C PO

13 SUPERVISED LEARNING Univariate approach (Neurophysiology) - correlation measures - Test of variance Metric-based approaches (metric learning) - Mahalanobis distance d m,n = (ξ m μ n )Σ n (ξ m μ n ) T - K-nearest neighbors (test on k) LDA J w = wt S B w w T S W w (Fisherbrains) BP Neural Networks (1 hidden layer)

14 DISCUSSION Most of explored datasets have been recorded within BCI context, using BCI protocols 1 recording session for each subject enrollment test Key issue: what are we modelling and discriminating? acquisition session (noise), recording instrumentation (different equipements used) individual specific EEG features (physiological trait)

15 DISCUSSION ongoing and questions: Better protocol/task Event related potentials (P300s, VEPs) Speech imagery (sensorymotor cortex) Motor imagery (sensorymotor cortex) Resting state with closed or open eyes (CE/OE)

16 DISCUSSION ongoing and questions: Better feature engineering Better supervised ML ex: model dependancies between channels ex: fusion rules in multibiometrics

17 DISCUSSION ongoing and questions: What can be done exploiting unsupervised learning and transfer learning? Unsupervised learning of individual-specific structures raw data hand-selected features Unsupervised learning of common structures remove from raw data How to use information available for some recordings?

18 INPUT Longitudinal data: short- and long-time variations t 0 t 0 +1week t 0 +1month t 0 +6months t 0 +1year Dataset size: 50 subjects Performed protocols: Motor imagery Speech imagery P300 Mental calculation Resting state Added information Age Handedness Gender

19 GOAL Extract individual-specific stable structures in EEG data Exploit advances in ML to find these structures

20 Thanks for your attention