Evaluation criteria or Quantifying the information content of the BCI feedback
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1 Evaluation criteria or Quantifying the information content of the BCI feedback!"#
2 Scheme of a BCI OFFLINE Measure of separability Classification and performance analysis Classification Time signal processing feature extraction Feature selection ONLINE / REALTIME Classification and feedback generation signal processing feature extraction Paradigm
3 Paradigm with discrete feedback feedback (FB) beep Fixation cross CUE FB s Classifier band power and LVQ Pfurtscheller et al., 1997
4 Generate feedback EEG processing & Feature extraction Linear or quadratic combiner Y C3 (t) e.g. AAR estimation a C3 (t) D(t)=w T.a(t) +w 0 TSD(t) Y C4 (t) e.g. AAR estimation a C4 (t) Static classifier: Schlögl et al Adaptive Classifier: Vidaurre et al. 2004
5 Paradigm with continuous (in time and magnitude) feedback feedback (FB) beep Fixation cross Fixation cross CUE FB s Classifier AAR parameters and LDA Schlögl et al. 1997, Neuper et al., 1999
6 Time course of the error rate with crossvalidation (red) w/o crossvalidation (cyan) BCI4c, F3-10, RLS,p=10, UC=0.006, C3+C4, 80L+80R
7 SNR, ROC, Confusion matrix True positives TP False positives FP True negatives TN False negatives FN Area under the curve (AUC) Wikipedia
8 Signal-to-Noise ratio (SNR) and Mutual Information SNR = signal/noise = s/n = (s+n)/n 1 SNR = (µ 1 -µ 2 )²/2(σ 2 ²+σ 1 ²) MI [bit] = ½*log 2 (SNR+1)
9 Inter-trial variance and between class differences Distance d d(t) = Σ i a i (t)*w i +w 0 is continous in time and in magnitude BCI4c, F3-10, RLS, p=10, UC=0.006, C3+C4, 80L+80R
10 SNR = Mutual Information = Transfer information Entropy [bit] I t = ½*log 2 (S t /N t +1) BCI4c, F3-10, RLS,p=10, UC=0.006, C3+C4, 80L+80R
11 Schlögl et al. 2002
12 Question Which method should be implemented online? Which criteria to use? ERR[%] or MI[bit] or something else?
13 Error rate & Mut. Information Error [%] A B C D E F G H I J t i m e [ s ] Mutual information [bit] A B C D E F G H I t i m e [ s ]
14 Steepness of mutual information speed (i.e. time delay) combined with classification accuracy Evaluation of signal processing (feature extraction and classification)
15 ERR[%] vs. MI [bit] ERR% uses only the SIGN and ignores magnitude Error rate is limited by 0%, Err% close to 0% resolution is limited by N MI takes into account the information of the magnitude MI no upper limit, can indicate large SNR => MI
16 Cohen s Kappa coefficient confusion Matrix H of size MxM for M classes N samples n oi, n io : sum of rows and columns p e chance expected agreement se(kappa) standard error, confidence interval Useful for more than 2 classes BCI competition 2005, data set IIIa Schlögl et al. (accepted) H i ii ACC = p 0 = N n n i oi io pe = N N = se ( ) = p o p 1- + p 2 e 0 p p e e [ n n ( n + n )] i oi io oi io ( 1 pe) N N 3
17 ACC [%], KAPPA ± SE k Accuracy [%] kappa ± s.d. [%] Accuracy [%] kappa ± s.d. [%] Accuracy [%] kappa ± s.d. [%] t [s] t [s] k t [s] Accuracy [%] kappa ± s.d. [%] Accuracy [%] kappa ± s.d. [%] Accuracy [%] kappa ± s.d. [%] t [s] t [s] l t [s] Accuracy [%] kappa ± s.d. [%] Accuracy [%] kappa ± s.d. [%] Accuracy [%] kappa ± s.d. [%] t [s] t [s] BCI competition 2005, data set IIIa t [s]
18
19 Summary of Signal processing & classification Workshop B Evaluation criteria
20 Evaluation criteria Classification accuracy (or Error rate) Receiver-operator-characteristics curve (ROC) Signal-to-noise ratio (SNR) Mutual information and Information transfer Steepness of the mutual information Cohen s Kappa coefficient Correlation coefficient R² Letter s per minute HF-difference
21 Questions Which criteria to use for evaluating feature extraction and classifiers? Which criteria should be used in Online BCI experiments?
22 ERR%/Accuracy, SNR, MI, Steepness of MI, Kappa (Alois) R², ITR, Letter per Minute, Duration until successful (Dennis McFarland) HF-Difference (Jane Huggins) ROC, AUC (Jeremy Hill)
23 Summary (1) Justification for criteria of the BCI competion ERR rate (Accuracy) Kappa Steepness of the mutual information
24 Summary (2) Agreement that Shannon s Communication theory should be applied Different models of the communication channel: Is the brain digital or analog?
25 Summary (3) Recommended evaluation of asychronous BCI + Confusion Matrix (TP,FP,TN,FN) and rebouncing time - Other proposed criteria are not recommended
26 Summary (4) Asking for software repository for the different evaluation criteria BIOSIG provides a starting point
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