1. CH 18. Adaptation in brain-computer interfaces

2. Introduction  Inherent nonstationarity of EEG  Why do we need ‘adaptation’ ? varies between BCI sessions and within individual sessions due to a number of factors : changes in background brain activity, fatigue, concentration levels, etc. depends on the skill and experience of subjects classifier trained on past EEG data : not optimal for other sessions need for adaptation in BCI

3. Introduction  Inherent nonstationarity of EEG : shift of the power of the selected frequency band in the calibration compared to the feedback session

4. Introduction  Online adaptation of the classifier keep the classifier constantly tuned to the EEG signal subject 의 생각을 판독하기 위해 그들에게 약 100 시간의 뇌 활동 조절 훈련을 시키는 대신, 개인별로 classifier 의 parameter 들을 조정함으로써 20 분 정도만 투자하면 된다. 따라서 신호처리기술을 이용해 적응의 부담을 subject 에서 기계로 전가할 수 있게 되었다.

5. Introduction  3 Studies Adaptation in CSP-based BCI systems (Offline study) : 3 subjects, mental typewriter Adaptive online discriminant analysis for cue-based BCI : 6 naive subjects, basket paradigm Online classifier adaptation in an asynchronous BCI : 1 subject, driving a wheelchair (Online study)

6. Study1. Adaptation in CSP-based BCI systems

7. Experimental Setup  BBCI system with visual feedback  3 subjects (2 naive subjects + 1 experienced subject)  features reflecting changes of bandpower  The experiments consisted of 2 parts … a calibration measurement : a feedback period : visual stimuli L, R, and F selection of 2 imagery classes and frequency bands (discriminability) CSP analysis & CSP filters calculation of a linear separation btw bandpower values (LDA) EEG from 64 channels bandpass-filter and common spatial filter measure of instantaneous bandpower (with sliding window) these values were weighted by LDA classifier → move a cursor

8. Mental Typewriter Feedback  a continuous movement of the cursor in the horizontal direction  type a letter on the basis of binary choices  symbol ‘<‘ for deleting one letter  after an error of choice → subjects relax or stretch

9. Adaptation algorithms  ORIG : unmodified classifier trained on calibration data  REBIAS : shift the original classifier’s hyperplane parallel to itself  RETRAIN : rotate the hyperplane  RECSP : classifier trained on feedback data Increasing order of change : ORIG < REBIAS < RETRAIN < RECSP In all adaptive methods, we need to make a trade off : Estimate the number of training samples necessary for retraining each method and each subject. Taking more training : more stable estimates, less adaptive

10. Results  Conclusion : Original classifier can hardly be outperformed by any relearning method.

11. Study2. Adaptive online discriminant analysis for Cue-based BCI

12. Principles

13. Principles  The adaptation trigger is Divided into 2 parameters : - Trigger start = T ini - Trigger stop  Adaptation window : the num- ber of samples btw trigger start and stop, ‘N’.  Adaptation starts at T ini and stops after the adaptation window.  Delay time for avoiding overfitting.  After the delay, classifier is updated.

14. Principles MI : Mutual Information mi : the output of the classifier UC tini : an update coefficient : maximum class-separability T ini : the time when appears  The update equations for Kalman filtering  Parameter initialization

15. Experimental Setup  6 naive subjects  Subjects performed motor imagery experiments – basket paradigm  1080 trials for each, (40 trials * 9 runs * 3 sessions = 1080 trials)  2-class cue-based and EEG-based BCI

16. Results  Experimental results, minimum ERR and maximum MI from single trial analysis of each session

17. Results  두 집단이 통계적으로 정말 다른 것인가? - Parametric test : 모집단이 정규분포를 이룰 때 - Permutation test : 모집단의 분포가 정규분포가 아닐 때

18.  Are continuously adaptive classifiers better than discontinuously adaptive ones ? Experimental Setup discontinuously adaptive LDA classifier 6 new subjects, basket paradigm 3 runs The 1 st classifier is the general classifier. LDA classifier is updated and used for 3 runs. 9 runs

19. Results  Minimum ERR and maximum MI of online & discontinuously adaptive LDA classifiers.

20. Results  Session comparison of discontinuously adaptive LDA classifiers  Comparison of discontinuously and online adaptive LDA classifiers

21. Study3. Online Classifier Adaptation in an Asynchronous BCI

22. Introduction  Previous preliminary works IDIAP BCI : performed in an asynchronous paradigm (CH.6) Online learning with the basic gradient descent algorithm on a Gaussian classifier Online learning with stochastic meta descent algorithm → adapting individual learning rates for each parameter → accelerates training

23. Principle (1) : Statistical Gaussian Classifier X : Sample C i : Class N i : Gaussian prototypes A: Total activation of the classifier a c : Activation of class c y c : Posterior probability of class c Decision : class with the highest probability ! under the threshold → result is “Unknown”.

24. Principle (1) : Statistical Gaussian Classifier  Training of the classifier starts from an initial model improved by stochastic gradient descent model (y i : Posterior probability of class i, t : target vector) For optimization, calculate the derivative of the error  The gradient descent update equations At each step, update center ( α ) and covariance ( β) of individual learning rate

25. Principle (2) : Stochastic Meta Descent Stochastic meta descent is an extension of gradient descent that uses adaptive learning rates to accelerate learning. However, in SMD algorithm, each parameter maintains and adapts an individual learning rate. This is in contrast to basic gradient descent, which uses a single learning rate for all parameters. Learning rate Sample  Update the learning rates :  Similar system for the covariance updates : (α : meta-learning rate, v t : gradient trace ),,

26. Experimental Results  IDIAP BCI  Computer simulation of driving a wheelchair avoiding obstacles  Subject was guided by an operator  Samples are not balanced btw classes and the length of time varies

27. Experimental Results  Comparison between… online classification and offline performance of static classifier  Online adaptation produces a final classifier that outperforms the initial classifier.

28.  Online classification rate  Offline performance of the final classifier The online classification rates track the EEG signal well, with no clear bias between classes. The final classifier perform well on the last part of the session, but less well on the early part of the session. → Online adaptation makes it possible to complete the task from the very 1 st trial. Experimental Results

29. Discussion  Online classifier adaptation would improve the performance of a BCI Because of the high variability in EEG signals.  But, no systematic study has been done to formally analyze the extent of signal variation through different stages in a subject’s usage of BCI.  Adaptive methods such as REBIAS, RETRAIN improve the classifier, but do not result in a significant increase of performance.  The main research issue is that adaptation when we don’t know the user’s intent. → Reinforcement learning  Reinforcement learning : we receive only occasional feedback on how well or poorly we are performing. → (1) the recognition of cognitive error potentials (2) contextual information about how well the device is operating

30. Video

31. Thank you