5 Study 2 - Error-Related Potentials
5.7 Conclusion
0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1 0.3
0.4 0.5 0.6 0.7 0.8 0.9 1
0 0
0
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P3 Acc
ErrP TPR
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45
0.5 0.6 0.7 0.8 0.9 80
100 120 140
P3 accuracy
Total time
ErrP Acc=0.30
0.5 0.6 0.7 0.8 0.9 80
100 120 140
P3 accuracy
Total time
ErrP Acc=0.39
0.5 0.6 0.7 0.8 0.9 80
100 120 140
P3 accuracy
Total time
ErrP Acc=0.47
0.5 0.6 0.7 0.8 0.9 80
100 120 140
P3 accuracy
Total time
ErrP Acc=0.56
0.5 0.6 0.7 0.8 0.9 80
100 120 140
P3 accuracy
Total time
ErrP Acc=0.65
0.5 0.6 0.7 0.8 0.9 80
100 120 140
P3 accuracy
Total time
ErrP Acc=0.74
0.5 0.6 0.7 0.8 0.9 80
100 120 140
P3 accuracy
Total time
ErrP Acc=0.82
0.5 0.6 0.7 0.8 0.9 80
100 120 140
P3 accuracy
Total time
ErrP Acc=0.91
ErrP error correction No error correction
0.5 0.6 0.7 0.8 0.9 80
100 120 140
P3 accuracy
Total time
ErrP Acc=1.00
Figure 5.13.Action time increased to 50s. The same remaining parameters as in figure 5.5 were used. Time factors for variable P3 accuracy and ErrP true positive rates. Lower values (bright values) correspond to fast communication speed. The blue line marks the break-even point where both methods perform equally well. In addition, 10% and 20% boundaries are shown which can be considered as significant improvemnt or degradation areas.
and the FPR is does not exceed 10%.
With regard to the previously reported single-trial accuracies for the ErrP detection in fig-ure 5.11, the method can be considered very useful in certain cases. An example would be the BCI controlled wheelchair (e.g. [Iturrate et al., 2009] or [Rebsamen et al., 2007]). Both authors were using the P300 signal to commit commands to a wheelchair that drives to a specific lo-cation. Naturally the action time, i.e. driving to the location, can take a significant amount of time compared to simple grasping of an object with a robot arm. When the action time is increased to 50s, a different picture emerges as seen in figure 5.13.
One last thing left to prove is the hypothesis, that the FPR of the ErrP classifier is the param-eter that can lead to significant performance degradation. In the beginning of this section it was shown, that the FPR controls path (2) (Figure 5.5), a feedback connection which causes the system to use more subtrial presentations than necessary and is not present in a BCI without error correction. And indeed, increasing this parameter to 15% (Figure 5.14(a)) and 20% (Figure 5.14(b)) of the initial 10% and computingΘ100over a range of parameter values results in a serious performance drop as can be seen in figure 5.14(a) and 5.14(b).
5.7 Conclusion
0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1
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0
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P3 Acc
ErrP TPR
−0.08
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−0.02 0 0.02 0.04 0.06 0.08 0.1
(a) ErrP FPR set to 15%.
0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1
0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
0
0
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P3 Acc
ErrP TPR
−0.12
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(b) ErrP FPR set to 20%.
Figure 5.14. Increasing the false positive rate of the ErrP classifier leads to significant communication speed loss of the BCI.
error correction method has been proposed that could potentially help to improve the com-munication speed of BCIs. A classification method has been developed that is able detect the presence of an ErrP using only one EEG epoch as input data. Based on this method, 12 combinations of preprocessing and feature extraction methods have been compared which revealed that ICA based preprocessing and downsampling combined with a t-test for feature selection outperformed the remaining methods with mean accuracies of 85% across all 5 sub-jects. Given that P300 classification accuracy using an average over multiple subtrials usually resides in the range of 80-90% accuracy, this can be considered as a surprisingly good result.
The data obtained from the ROC curves in figure 5.11 showed that accuracies of 85−90% are possible to achieve while even keeping the false positive rates under 10%.
In addition, the feasibility of error correction has been studied using a probabilistic model of a generic BCI with automatic error correction. For BCI’s without error correction, the av-erage time to communicate a command can be calculated very easily since the BCI’s con-trol flow can be described as a simple feed-forward structure which depends only on the classifier’s correct rate. This task becomes more complicated for BCI’s with automatic error correction which introduces internal feedback loops. Using the proposed model it is now possible to predict the time between consecutive correct commands and thus compute the average time the BCI needs to correctly communicate an arbitrary number of commands.
The model accounts for the internal feedback loops and is able to predict in which cases the BCI will get stuck in the error correction feedback loops. It was also shown, that the average time to spell an error free sequence of commands, which requires deleting wrong commands and communicating the correct one afterwards, can be described as a discrete-time infinite Markov chain. This advanced model accounts for manual user corrections of
erroneous BCI commands. As an example, spelling the word[HELLO]might be the result of the command sequence[H,E,L,L,Z,Backspace,I,Backspace,O]. From this sequence of 9 commands, only 2 are erroneously recognized commands (Z andI). For every wrong command, aBackspacehas to be entered to correct for the mistake. In this sense, the infi-nite Markov chain can predict the time required to enter the the wordHELLOwhile also con-sidering the time required for the correction of erroneous commands. Using this theoretic framework it is now possible to directly compare the communication time for different BCI’s.
In contrast to the usual measure ofbits/sec, the outcome has more practical relevance since knowing the raw bits/sec carries no information about the practical communication speed in terms of time to communicate a sequence of commands correctly.
In a simulation study, the performance difference between a BCI with error correction and without error correction has been investigated. P300 detection accuracy and ErrP true pos-itive rates served as free parameters to highlight their effect on the overall communication time. The results showed, that ErrP true positive recognition rates must rise with P3 clas-sification accuracy if a performance improvement should be achieved. For tasks where the action time is long, e.g. for wheelchair navigation similar to [Rebsamen et al., 2007] where the action ends when the wheelchair arrived at the selected destination, error correction is highly feasible since it mainly avoids excessive execution of wrong actions. Further, the ex-periment validated that thefalse positive rate (FPR)of the ErrP classifier is the parameter that can cause a BCI with error correction to perform significantly worse than without error cor-rection. The usage of the proposed method extends even into the performance optimization domain. Since the global system performance can be predicted for any classifier threshold (i.e. all possible TPR/FPR combinations), a parameter set can be found that minimizesΘand thus minimizes mean communication times. The search space for the TPR/FPR combina-tions consists of the ROC curve of the respective ErrP classifier which can be obtained from the training set.
A limitation of this model is the assumption that the input data to for the BCI do not change over time, or more specifically that classification accuracy remains constant over time. Fa-tigue of the user or an increasing lack of concentration might lead to degraded classification performance in the intent recognition and error correction phase which might invalidate the model parameters.
To summarize, the goal of implementing an ErrP based error correction system for BCIs has been achieved. Important aspects of the feasibility of the error correction system have been studied which resulted in the development of a probabilistic model to compute the BCI’s communication time per correct symbol. The use of this theoretic framework might become more evident in the future for optimizing classifier thresholds in a complex BCI and as a com-parison measure whose accuracy does not suffer from low population sizes as it is common in real experiments. The effect of the involved parameters on the overall performance has been investigated which will prove to be useful for other researchers when it comes to optimize their own BCI’s performance. A single-trial classification method has been proposed which performed reasonably well with more than 80% correct rate in an offline setting. In
conclu-5.7 Conclusion
sion it can be ascertained, that tasks which involve long action execution times, automatic error correction offers a great communication speed improvement. Thus, the proposed sys-tem might be of great value for other researchers who apply ErrP based error correction with P300 BCIs in a setting which involves long command execution times. When optimizing the system with respect to communication time for correct commands, the probabilistic frame-work presented in this chapter will be useful for adapting the classifier’s threshold. This prob-lem which was usually solved by choosing a point on the ROC curve with heuristic optimality constraints as in [Takahashi et al., 2010] while the presented method is able to analytically determine the optimal operating point and even accounts for manual corrections.
This thesis was motivated by the question how a P300-based BCI can be used to efficiently control reaching and grasping movements of robotic actuators. A solution for this problem requires that the termefficientlyhas to be clearly defined. When talking about an efficient brain controlled robotic actuator it should imply that
1. Operation of the system should add as few additional cognitive load as possible 2. Operation should be intuitive and effortless
3. No subject training should be required
4. Translation of mental commands should be fast and reliable
In the following, the design of a novel system to control robotic devices in a natural way by brain signals will be presented. Initial design decisions and principles regarding the em-ployed paradigm will be discussed as well as the subcomponents involved in the whole pro-cess of detecting the users intentions down to the low-level control of the robotic endeffec-tors.