An Efficient P300-based Brain-Computer Interface with Minimal Calibration Time

1. An Efficient P300-based Brain-Computer Interface with Minimal Calibration Time Fabien LOTTE, Cuntai GUANBrain-Computer Interfaces laboratory Institute for Infocomm Research (I2R)Singapore

2. Introduction • Brain-Computer Interfaces (BCI) [wolpaw02] • Communication devices based on brain activity only • Very promising tools for severely disabled people • Mostly based on ElectroEncephaloGraphy (EEG)

3. P300-based BCI • The P300 • Positive potential occurring~ 300 ms after a rare and relevant stimulus • Very useful for assistive BCI applications • Spellers [Farwell88, Nijboer08] • Wheelchair control [Rebsamen07, Iturrate09] P300 Average EEG signal at electrode Cz

4. limitation and objective • Current BCI require long calibration times • Many examples of EEG signals needed to calibrate the BCI (supervised learning) • Re-calibration may be necessary on a regular-basis • Inconvenient and uncomfortable for the user • A problem for disabled users with limited attention span [birbaumer06] Objective of this paper:Reducing the calibration time of P300-based BCI

5. State-of-the-art • Few attempts to reduce calibration time in P300-based BCI • All based on online adaptation [Li08, Liu09] • Standard initial training with few examples • Online adaptation based on semi-supervised learning • Limitation • Poor initial performances • We propose a simple but efficient method to design P300-based BCI from few EEG examples

6. Our P300-based BCI design • Preprocessing • Segment from 150 ms to 500 ms after the stimulus • Segment around the P300, if any • Low-pass filtering below 25 Hz • The P300 is a slow wave • Downsampling to 50 Hz • A first dimensionality reduction • Classical preprocessing [Krusienski06, Thulasidas06] • Such processed EEG are usually directly used as features

7. Feature Extraction with Canonical Correlation Analysis • Canonical Correlation Analysis (CCA) [Hardoon04] • Find the directions wx and wy maximizing the correlation between the variables X and Y • Solved by eigenvalue decomposition • Feature extraction • Use CCA to find the directions weeg which maximize the correlation between the EEG and the class labels • The features are the EEG projected on weeg

8. Regularized CCA • Solving CCA requires the estimation of the data covariance matrix C • Problem • Few training examples => poor estimation • Solution • Regularization • Automatic process with [Ledoit & Wolf 04]

9. Classification • Linear Discriminant Analysis (LDA) • Most used and efficient classifier for P300-based BCI[Krusienski06] • Also based on covariance matrix estimation • Regularized LDA (RLDA) • Automatic regularization using [Ledoit & Wolf 04]

10. Evaluation • Single trial analysis of P300 data [Thulasidas06] • 10 healthy subjects • 8 EEG channels • 41 training & testing characters • 1 character includes • 20 P300 EEG epochs • 100 non-P300 EEG epochs • Evaluation for various training set sizes • ROC analysis: Area Under the ROC curve • Comparison of CCA with PCA • 50 features extracted from 136 initial EEG samples

11. Results

12. Results

13. Results

14. Results

15. Results

16. Conclusion • New design for P300-based BCI • Regularized CCA for feature extraction • Regularized LDA for classification • Simple to implement and computationally efficient • Requires much less training data for high performance => reduced calibration time • Future work • Online evaluation with disabled users • Joint CCA-LDA optimization

17. Thank you for your attention! Any question? Fabien LOTTE http://sites.google.com/site/fabienlotte/fprlotte@i2r.a-star.edu.sg Acknowledgements • Dr. David R. HARDOON • Dr. Brahim HAMADICHAREF

19. All curves