EEG Signal Acquisition, De-Noising and Classification for Brain Computer Interfaces

1. EEG Signal Acquisition, De-Noising and Classification for Brain Computer Interfaces Pavan Ramkumar Girish Singhal Department of Electronics and Communication Engineering Indian Institute of Technology Guwahati Supervisor: Dr. S. Dandapat

2. Project Overview Neurological signals as a Biometric • Biological systems offer potential features for uniquely characterizing individuals. • High entropy and uniqueness of the neurological activity are sparsely explored for biometric authentication. Why Electroencephalogram (EEG) Signal? • In contrast to global anatomical information obtained from expensive imaging (fMRI), EEG offers high density functional information specific to the mental task. • Acquisition system is relatively inexpensive to build.

3. Project Overview… Motivation • Physiological studies have tried to investigate correlation between EEG and genetic information [Vogel, 1970]. • more secure, non-manipulative • Identity can be established in real time (unlike bio chemical tests DNA etc.) Goal • Real-time implementation of biometric authentication for a closed set of users.

4. Project Overview… Prior Art using EEG • M. Poulos et. al. Task: eyes closed, Features: AR model, FFT, Bilinear model, Classifier: LVQ • R. Palaniappan et. al. Task: Viewing of standardized images [Vanderwart, 1980] relevant to memory and cognition, • Features: PSD, Classifier: ANN Our work • Task: Data fusion tasks are highly cognitive. We have experimented with stereopsis (binocular fusion for depth perception) • Features: Linear Prediction Model • Classifier: Two stage classification using K-NN and SVM.

5. Motivation : Selecting of appropriate task , features and classifying machine Issues : Comparative study of perceptive (stereopsis) and non-perceptive (mental arithmetic) tasks , scope of rejection in classifier. Motivation : De-noising from contributing bio-signals , improving soundness of features extracted Issues : Choice of optimization algorithm and objective function for ICA Motivation : Development of a real time system Issues : Amplification , Digitization . De-noising Modular Flow

6. Protection Circuit Impedance Matching, Clamping Diodes for user safety Differential Gain Two stage Amplifier : total gain : 10,000 Stage 1 : I.A. (AD521) , Gain: 25, CMRR: 100dB Stage 2 : HG non-inverting amplifier (~ 500) Line noise suppression Tunable Q Twin Tee Notch with feedback DC Drift accumulation at electrodes Prevention of saturation Two stage Sallen Key High Pass filter (0.1Hz, -80dB/decade) Digitization and Anti-aliasing Sallen Key Low Pass filter (70Hz, 40dB/decade). Digitization using PCL-HG818 card (200Hz, pacer trigger, 5Vp-p) Module I: Acquisition System Objective: To develop a real time EEG acquisition system. Design Issue Solution

8. Future Work • Driven Right Leg Circuit • High CMRR • Shielded probes • FET based instrumentation amplifiers • Differential amplifiers have high input impedance • Negligible input bias current • Low amplifier noise • De-noising using software • Discrete Filtering • Independent Component Analysis (ICA)

9. Module II : Design of Experiments Objective: To design experimental tasks that maximize uniqueness of features extracted. What are the desired characteristics? • Must be easy to perform for user • Designing universal protocol for time invariability 􀂾 standardized lighting conditions 􀂾 gaze localization 􀂾 texture invariance 􀂾 body posture constancy

10. Stereopsis What is Stereopsis? • Computation of visual depth from retinal / binocular disparity • Signals from each retina reach the visual cortex via independent pathways

11. Stereopsis… Why Stereopsis? • Highly cognitive task involving multi-sensor integration • Binocular fusion shows a higher FFT activity as observed from electrode P4 in MEG recordings. • Pre-cognitive perceptive task and hence, resulting EEG patterns are relatively immune to voluntary distortion by un co-operative users. Hypothesis: May provide better features

12. Stereopsis… MEG Results from Literature Courtesy: U. Shahani et al. / Neuroscience Letters 315 (2001) 154–158

13. Stereopsis… • Verification of MEG results • EEG recordings at IIT Guwahati • Stereograms are used to eliminate monocular cues from depth.

14. Verification using EEG… Signals from parietal region

15. Verification using EEG… Signals from occipital region

16. Verification using EEG… Sum of STFT over windows (Σ log|X(n, k)|)

17. EEG Signals acquired during Stereopsis Task , for four subjects

18. FFTs of EEG Signals acquired during Stereopsis Task , for four subjects

19. Subject Identification Experiments Experiment 1: A Perceptive Task Binocular viewings of ‘Cyclopean’ Wallpaper images (using industrial BIOPAC system at IIT Guwahati). 3 subjects, 1 trial, 1 minute recordings, sampled at 200Hz, each of fused (vs) non-fused viewings from O1, O2, P3, P4 regions. Experiment 2: A Non-perceptive Task Mental Arithmetic (CSU Dataset). 3 subjects, 10 trials, 10 second recordings, sampled at 250Hz, each from C3, C4, O1, O2, P3, P4 regions. Why Mental Arithmetic? To compare perceptive and non-perceptive computationally complex tasks

20. Future Work • Design and conduct binaural perception experiments • Controlled environments for data collection

21. Module III : Feature Extraction and Classification Objective: To extract features most relevant to given task and optimize on the parameters of classifiers to maximize accuracy. • What are the desired characteristics? • • Feature Extraction • Parametric and Non- parametric (Accuracy Vs Task Invariance) • Time Complexity • • Classification • Reject option : degree of accuracy • Adaptive to dataset augmentation • Real time implementable

22. Literature Survey • • Features used • channel wise PSD • AR Model • Non-parametric FFT peaks • • Classifiers used • LVQ • Fuzzy ART Results Task I : Eye closed , M. Poulous , 1999 80-100 % Task II : Viewing of standardized images , R Palaniappan; K V R Ravi , Dec. 2003 94.18 %

23. Feature Extraction • Features used: • Window length = 1second, Overlap size = ½ second • 70 Linear Prediction Coefficients per window. • Description of feature space • Stereopsis experiment: • For each subject: • 60 seconds of data from 4 channels per subject • 120 patterns per subject • Each of dimensionality 70 × 4 = 280 • 80 are used for training and 40 for testing. • Mental-Arithmetic experiment: • For each subject: • 100 seconds of data from 6 channels • 200 patterns • Each dimensionality 70 × 6 = 420 • 100 are used for training and 100 for testing.

24. Determination of LP order for Mental Arithmetic task Prediction error converged at p = 6 in Levinson – Durbin Algorithm

25. Determination of LP order for Stereopsis Task LP order 15 and 25 are found to be reasonable guesses for Stereopsis Task

26. Classification scheme • Classifiers used: • Multi-class SVM with Voting • 2 stage, KNN - SVM • 2 stage, Weighted KNN - SVM • Empirically determined parameters: • K = 14 nearest neighbors • RBF Kernel Function with σ = 0.5

27. Determination of k for Weighted K-NN K = 14 was found to give least error on a bootstrapped dataset

28. Results (with LPC, 70 per channel) Three Class Problem Four Class Problem

29. LPC (vs) LPCC • LPCCs are weighted average of LPCs : More consistent • LPCC have been found to give better biometric features • Lower dimensionality of feature space • Hence, search space decreases, computationally faster

30. Feature Extraction • Features used: • Window length = 1second, Overlap size = ½ second • 6 Linear Prediction Cepstral Coefficients per window. • Description of feature space • Stereopsis experiment: • For each subject: • 60 seconds of data from 4 channels per subject • 120 patterns per subject • Each of dimensionality 6 × 6 = 24 • 80 are used for training and 40 for testing. • Mental-Arithmetic experiment: • For each subject: • 100 seconds of data from 6 channels • 200 patterns • Each dimensionality 6 × 6 = 36 • 100 are used for training and 100 for testing.

31. Determination of k for Weighted K-NN K = 14 was found to give least error on a bootstrapped dataset

32. TASK : Arithmetic task Stereopsis Scheme/Error With LP order = 6 With LP order = 25 , 15 Weighted K-NN 7.07% 3.42% ,4.27 % Results (with LPCC) Three Class Problem Four Class Problem TASK : Arithmetic task Stereopsis Scheme/Error With LP order = 6 With LP order = 25 , 15 Weighted K-NN 12.88% 6.41 % , 8.33 %

33. Summary Mental Arithmetic task • Within class accuracies for 4-person set with LPCC vary between 87% to 99% • Within class accuracies for 3-person set with LPCC vary between 88% to 100% • Overall accuracies for 4-person set with LPCC reach upto 88% • Overall accuracies for 3-person set with LPCC reach upto 93% Stereopsis Task • Within class accuracies for 4-person set with LPCC vary between 87% to 100% • Within class accuracies for 3-person set with LPCC vary between 93% to 98% • Overall accuracies for 4-person set with LPCC reach upto 94% • Overall accuracies for 3-person set with LPCC reach upto 97%

34. Future Work • Use of non-linear models for feature extraction • One against all scheme • + High accuracy in 2 class problem ~ 96%) • - Re-training of all SVMs for new entry • Ensemble of k-means clusters • Handles non-uniform distribution of training set