1. An Efficient P300-based Brain-Computer Interface with Minimal Calibration Time
Fabien LOTTE, Cuntai GUAN Brain-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
For P300 DETECTION

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
The features are linear combinations of the initial EEG
We obtain new features, most likely most discriminative
We can obtain less features, but using only a small number of directions => better for small sample size learning

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
Results averaged over the 10 subjects

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?
Acknowledgements
Dr. David R. HARDOON
Dr. Brahim HAMADICHAREF
Fabien LOTTE

19. All curves