Anticipation increases the efficiency of a daily task by partial advance activation of neural substrates involved in it. Single trial recognition of this activation can be exploited for a novel anticipation based Brain Computer Interface (BCI). In the current work we compare different methods for the recognition of Electroencephalogram (EEG) correlates of this activation on single trials as a first step towards building such a BCI. To do so, we recorded EEG from 9 subjects performing a classical Contingent Negative Variation (CNV) paradigm (usually reported for studying anticipatory behavior in neurophysiological experiments) with GO and NOGO conditions. We first compare classification accuracies with features such as Least Square fitting Line (LSFL) parameters and Least Square Fitting Polynomial (LSFP) coefficients using a Quadratic Discriminant Analysis (QDA) classifier. We then test the best features with complex classifiers such as Gaussian Mixture Models (GMMs) and Support Vector Machines (SVMs)

Chavarriaga, Ricardo

Garipelli, Gangadhar

Millán, José del R.

Anticipation increases the efficiency of a daily task by partial advance activation of neural substrates involved in it. Single trial recognition of this activation can be exploited for a novel anticipation based Brain Computer Interface (BCI). In the current work we compare different methods for the recognition of Electroencephalogram (EEG) correlates of this activation on single trials as a first step towards building such a BCI. To do so, we recorded EEG from 9 subjects performing a classical Contingent Negative Variation (CNV) paradigm (usually reported for studying anticipatory behavior in neurophysiological experiments) with GO and NOGO conditions. We first compare classification accuracies with features such as Least Square fitting Line (LSFL) parameters and Least Square Fitting Polynomial (LSFP) coefficients using a Quadratic Discriminant Analysis (QDA) classifier. We then test the best features with complex classifiers such as Gaussian Mixture Models (GMMs) and Support Vector Machines (SVMs).LIDIAPPublished in In proceedings, 4 th International Brain-Computer Interface Workshop and Training Course 2008

Infoscience - École polytechnique fédérale de Lausanne

  ESEARCHREPRORTIDIAPRue du Simplon 4IDIAP Research Institute1920 Martigny − Switzerlandwww.idiap.chTel: +41 27 721 77 11 Email: info@idiap.chP.O. Box 592Fax: +41 27 721 77 12Recognition of AnticipatoryBehavior from Human EEGGangadhar Garipelli a bRicardo Chavarriaga a Jose´ del R. Milla´n a bIDIAP–RR 08-52June 27, 2008published inIn proceedings, 4 th International Brain-Computer Interface Workshopand Training Course 2008.a Idiap Research Institute, P.O.Box 592, 1920, Martigny, Switzerlandb Ecole Polytechnique Fdrale de Lausanne (EPFL), SwitzerlandIDIAP Research Report 08-52Recognition of Anticipatory Behavior fromHuman EEGGangadhar Garipelli Ricardo Chavarriaga Jose´ del R. Milla´nJune 27, 2008published inIn proceedings, 4 th International Brain-Computer Interface Workshop and Training Course 2008.Abstract. Anticipation increases the efficiency of a daily task by partial advance activationof neural substrates involved in it. Single trial recognition of this activation can be exploitedfor a novel anticipation based Brain Computer Interface (BCI). In the current work we comparedifferent methods for the recognition of Electroencephalogram (EEG) correlates of this activationon single trials as a first step towards building such a BCI. To do so, we recorded EEG from 9subjects performing a classical Contingent Negative Variation (CNV) paradigm (usually reportedfor studying anticipatory behavior in neurophysiological experiments) with GO and NOGO con-ditions. We first compare classification accuracies with features such as Least Square fitting Line(LSFL) parameters and Least Square Fitting Polynomial (LSFP) coefficients using a QuadraticDiscriminant Analysis (QDA) classifier. We then test the best features with complex classifierssuch as Gaussian Mixture Models (GMMs) and Support Vector Machines (SVMs).2 IDIAP–RR 08-52Contents1 Introduction 32 Methods 32.1 Experimental setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32.2 Data acquisition and preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32.3 EEG grand averages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 Classification 53.1 Feature selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53.2 Classifiers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 Results 65 Conclusions 76 Acknowledgements 8IDIAP–RR 08-52 31 IntroductionAnticipation is a process that not only depends on past and current states but also on future expecta-tions. Without anticipation everyday cognitive tasks would become exclusively reactive. Conversely,this process increases the efficiency of daily tasks by partial advance activation of the neural substratesinvolved [6]. We hypothesize that the recognition of this activation can be exploited for Brain Com-puter Interaction (BCI). For example, consider a scenario of a brain-actuated wheelchair [7] drivingtowards a table with breakfast lying among several other tables. Using the onboard sensors the intel-ligent controller in the wheelchair can detect the presence of a table in front but it cannot decide byitself whether to dock or avoid. If the controller is integrated with anticipation recognition algorithms,the user can issue the docking command just by anticipating the docking event to happen. Otherwise,the controller triggers the obstacle avoidance behavior.To the best of our knowledge, anticipation related potentials in human EEG are well studied inthe context of clinical science and functional neurophysiological studies [8] but not well explored inthe context of BCI, excepting one early attempt based on neurofeedback [2]. In the current work wecompare different methods for the recognition of these potentials on single trials as a first step towardsthe design of an anticipation-based BCI.To record these potentials we have considered a classical Contingent Negative Variation (CNV)paradigm [8] as an experimental procedure. A vast amount of literature describes the CNV potentials(the potentials recorded using CNV paradigm) as related to anticipation [8, 4, 5]. In this paradigma warning stimulus (S1) predicts the appearance of an imperative stimulus (S2) in a predictableinter-stimulus-interval (ISI). A negative shift in the cortical activity with a centro-medial distribution(under the vertex electrode, Cz) usually develops between S1 and S2 depending on contingency ofstimuli and task parameter relevance [4, 5]. This signal has been shown to be stable over several daysand in different conditions (e.g., amount of sleep time) [4]. In addition, one neurofeedback experimentsuggests that humans are able to modulate its amplitude [2]. The stability of this potential, andthe human’s ability to modulate its amplitude, support the possibility of using this phenomenon forthe design of anticipation-based BCI. To this end, it is first necessary to ascertain the feasibility ofachieving reliable recognition of CNV potentials on single trials; this is the goal of the present study.In the following sections we describe the experimental setup, along with classification techniques usedin recognizing these potentials.2 Methods2.1 Experimental setupWe used the CNV paradigm with relevant (GO) and irrelevant (NOGO) conditions for simulatinganticipatory and non-anticipatory behaviors (e.g., the table with food corresponds to a relevant con-dition). Fig. 1(a) and Fig. 1(b) describe the CNV paradigm used in the current study. The EEGsignals of nine male subjects (22-27 yrs.) were recorded in four consecutive sessions with 50 trials each(equiprobable GO and NOGO trials in random order separated by an inter-trial interval of 4± 4s).2.2 Data acquisition and preprocessingThe EEG signals were acquired for 9 subjects using 32 (subjects 4, 5 and 9) or 64 (remaining 6subjects) electrodes according to the 10/20 international system with a sampling rate of 512Hz. RawEEG signals were first spatially filtered by using common average reference (CAR). The signals werethen filtered using a low pass filter with cut off frequency of 15Hz and then the trials were extractedand separated into GO and NOGO trials using S1 as the reference (i.e., onset of S1 considered as at0.0s) with [-1.0 5.0]s as total trial interval. Average voltage of the time window from -1000ms to 0mswas used as a baseline.4 IDIAP–RR 08-52(a) (b)(c)Figure 1: CNV experimental setup and ERP grand averages. (a) In the GO condition a warningstimulus (S1) with a green dot at time t = 0s is displayed and then an imperative stimulus (S2) witha red dot on the screen is presented with ISI of 4s. Subjects are instructed to anticipate and press abutton as soon as S2 is presented. (b) To differentiate the NOGO condition from the GO conditionS1 is replaced with a yellow dot. The subjects are instructed to do nothing for this condition. (c) Thegrand averages of GO and NOGO trials for six subjects recorded with 64 electrode configuration atCz electrode. The circular figures are the topographic representation of average scalp distribution atdifferent time scales for GO (bottom) and NOGO (top) conditions.2.3 EEG grand averagesThe EEG grand averages at Cz electrode computed over subjects for GO and NOGO conditions showclear differences (see Fig. 1(c) for grand averages using 64 electrode set-up). Similar differences areobserved in the case of 32 electrode set-up. From the topographic plots of average scalp distributionwe observed an increasing negativity under this electrode in GO condition and a smaller negativityin NOGO condition. An evoked response due to S1 is observed at this electrode around 0.3s to 0.4sin both conditions. The potential at Cz during GO condition is composed of an early peak around 1sand a late peak between 3.5s and 4.0s which is consistent with previous studies [5]. Although cleardifferences are observed in grand averages, the use of these potentials for BCI imposes the challenge ofrecognizing them on single trial. The methods developed for addressing these challenges are describedIDIAP–RR 08-52 5in the next section.3 ClassificationAs the subjects were instructed to press a button on the arrival of S2 (at 4s) the recognition methodsevaluated here are based on the EEG potentials up to 3.5s after the onset of S1 (0.0s) in order to avoidany movement preparation potential that could contaminate the recognition of anticipation processes.In the scope of the current work we restrict to features computed from the potential at Cz electrodealone (vCz(t), where t is time, t ∈ [0 Tmax] and Tmax = 3.5s).3.1 Feature selectionSince slope and peak negativity are usually reported as features of CNV potentials [6], we first testLeast Square Fitting Line (LSFL) parameters with the help of a Quadratic Discriminant Analy-sis (QDA) classifier. We then compare higher order features such as Least Square Fitting Poly-nomial (LSFP) features computed as the coefficients of nth order LSFP (αi, where i= 1,...n ofv˜Cz = α0 + α1t1 + α2t2 + . . .+ αntn). Each trial is then described by feature vector, x = [α0 α1 α2 . . . αn]T ,where, x ∈ Rn+1. The best polynomial order for each subject is chosen by comparing training accu-racies of classifiers calculated for n ∈ {2, 3, ..., 6} (the maximal order for search is 6 due to the limitedamount of training samples). The LSFL features are equivalent to the LSFP features with order one.3.2 ClassifiersWe compare classification of anticipation related potentials using the features described above withthe help of different classifiers described in the following paragraphs. Due to space limitations we giveonly a very brief introduction of these classifiers.Quadratic Discriminant Analysis (QDA). Similar to Linear Discriminant Analysis (LDA) clas-sifier, the QDA classifiers assumes that the features are normally distributed and relaxes the assump-tion that the covariance of each class is identical. For the current problem, we first project the featuresonto a canonical space with the help of a projection matrix, that maximizes between-class varianceand minimizes within-class variance, which can be obtained by maximizing Fisher’s criterion [1]. Wethen calculate QDA classifiers on the projected features.Gaussian Mixture Model (GMM). The GMM is a generative model widely used for clusteringand classification applications. In the current study we first model each class distribution with aseparate GMM and using these models we build a classifier. The initial estimates of means areobtained using a k-means algorithm and we then use the Expectation Maximization (EM) algorithm[1] for estimating the mean (µk), covariance (Σk) and mixing coefficients (pik) of each Gaussiancomponent of the GMM. Due to limited number of training samples we reduce the number of freeparameters to estimate by constraining the covariance matrix to be diagonal and sharing it among allthe components. The best number of Gaussian components for each subject is obtained by exhaustivesearch in the range {1, 2, ..., 4} based on training accuracies. Since this classifier suffers from theproblem of local minima for complex data that are not well clustered, we built 100 different modelswith random initial Gaussian centers. The best model from all the 100 models was then chosen usingthe training accuracy and considered for testing with test data.Support Vector Machine (SVM). SVMs are supervised learning methods that simultaneouslyminimize empirical classification error and maximize the geometrical margin between two classes [3].In the present study we report classification results based on linear kernel (SVM-linear) and Radial6 IDIAP–RR 08-52Table 1: Comparison of cross-validation accuracies with different features and classifiers.LSFL LSFPSubject QDA QDA GMM SVM-linear SVM-RBF1 62.15±8.62 66.14±6.78 58.02±9.44 58.02±5.69 58.52±7.922 64.61±13.17 66.04±6.90 64.55±5.68 64.55±6.31 54.37±5.443 47.57±5.56 59.91±10.97 46.87±1.82 46.87±7.37 45.93±7.684 50.50±5.74 58.00±7.48 61.00±5.29 61.00±12.48 51.00±3.835 68.83±8.95 75.86±6.45 66.31±7.36 66.31±6.60 46.73±4.116 53.64±8.40 53.09±9.55 43.54±4.00 43.54±6.43 52.44±3.037 44.64±3.01 54.77±11.93 57.04±7.20 57.04±10.03 53.75±4.448 53.82±9.39 51.28±4.25 44.01±9.09 44.01±9.36 47.11±6.409 51.00±8.08 51.00±5.29 51.00±9.59 51.00±12.70 53.00±8.25Basis Function kernel (SVM-RBF). The free parameters of the classifier are estimated by 10-fold crossvalidation on training trials.4 ResultsTo assess the classification performance across sessions we did a 4-fold cross-validation study whereeach fold corresponds to a separate session. The results of this study are summarized in Table 1. Wefirst did feature comparison with the help of QDA classifiers trained separately for each subject. Weobserved that the LSFP features outperform the LSFL features (Wilcoxon test p=0.01, over all thesubjects and sessions), suggesting that these features describe the anticipation related potential betterthan the LSFL features that are usually computed for the characterization of the CNV potential inneurophysiological studies [6, 2]. It is worth noting that no specific differences are observed for EEGsetup with 32 or 64 electrodes.Since LSFP features performed better, we tested them on the other complex classifiers such asGMM and SVM-linear and SVM-RBF classifiers to compare with the performance of the QDA clas-sifier (see Table. 1). The classification accuracies of the QDA classifier are significantly better thanthe other three classifiers (Wilcoxon test, p=0.01 among all the subjects and sessions). Coming toindividual subjects, the accuracies for subjects 6, 8 and 9 are close to random for all the methods.For subjects 1, 2, 3 and 5 the QDA classifier with LSFP features performed significantly better thanthe other classifiers (the best being subject 5, 75.86±6.45% ). On average the SVM classifier withlinear kernel is the next best classifier. However, this classifier gives accuracies above 65% only forone subject whereas the QDA classifier does so for 3 subjects. The SVM classifier with RBF kerneland GMM classifier perform worse compared to the QDA classifier.Although the QDA classifier with LSFP features performs best compared to all the other methods,the results of cross-validation show that the recognition method is not reliable enough for a BCI.Nevertheless, most subjects exhibit an increasing performance over sessions (we excluded the subjects6, 8 and 9 from this study due to the classification accuracies are close to random in all sessionswith all the classifiers). Fig. 2 illustrates this trend with the help of accuracies averaged over allthe 6 subjects separately for each session. The accuracies in the 3rd and 4th session are significantlyhigher ( 67.06 ± 12.30% and 66.77 ± 7.77) as compared to the first two sessions (58.60 ± 13.96 and61.37± 5.61). Observing this trend, we argue that the features of this potential are becoming stableand well separable over time due to subjects adaptation to the experimental paradigm. This suggeststhat, similar to BCI systems, a good training method is a key component for subjects to learn toprovide stable EEG patterns. Proper training of the subjects is likely to enhance the classificationaccuracies with the methods studied in the current work.IDIAP–RR 08-52 7Figure 2: Average classification accuracies in different sessions using QDA classifier with LSFP featuresfor subjects 1-5 and 7.5 ConclusionsWe compared different classification techniques for the recognition of anticipation related potentialsfrom human EEG as a first step towards the design of a novel BCI. From the off-line studies on thesepotentials using the QDA classifier we observed that the LSFP features perform better than the LSFLfeatures. This result suggests that LSFP features describe the anticipation related potential betterthan the LSFL features, the latter being those usually computed in neurofeedback and neurophysio-logical studies for the characterization of CNV potentials [2, 6]. We also compared the classificationaccuracies of LSFP features with QDA, GMM, SVM-linear and SVM-RBF classifiers. The 4-foldcross validation, with each fold corresponding to a separate session, showed that the QDA classifiersperform significantly better than the other classifiersIt is worth noting that none of the subjects considered in the current study had previous experiencewith the CNV protocol. The systematic observation of the performance in each session showed anincreasing trend in classification accuracy for most (6 out of 9) subjects. We argue that this trend isdue to subjects’ adaptation to the experimental paradigm. However, for the remaining subjects theclassification accuracy is at chance level which is most likely due to lack of practice (note that theCNV paradigm is a learned task and subjects need to practice for a few sessions). As some subjectslearn faster than others the classification accuracies also differ in the same way. Moreover, as there isa learning component, the classifier calculated using the later sessions does not perform well for theearly session, whereas the classifier calculated using early sessions performs better for the later session(see Fig. 2).Proper training of the subjects is likely to enhance the performance of the recognition methodscompared in the current study. Moreover, an early study based on neurofeedback showing that thesubjects were able modulate these potentials [2]. Based on this knowledge we hypothesize that theclosed loop implementation of the current recognition methods can improve the subject’s trainingyielding to more stable and well separable features. We consider this implementation as next imme-diate step for further research. In addition, we will extend these methods to multi-electrode featuresin order to improve the classification accuracies. Also, fast recognition of these potentials is anothercrucial factor for building a reliable BCI application. We plan to achieve this by extending the cur-rent methods to multi-classifier based recognition techniques in which each classifier looks at differenttemporal blocks of EEG and makes a decision as quickly as possible.8 IDIAP–RR 08-526 AcknowledgementsThis work is supported by the EC-contract number BACS FP6-IST-027140 and the Swiss NationalScience Foundation NCCR “IM2”.References[1] C. M. Bishop. Pattern Recognition and Machine Learning. Springer, 2006.[2] L. Bozinovska, S. Bozinovski, and G. Stojanov. Electroexpectogram: Experimental design andalgorithms. In Proceedings IEEE Biomedical Engineering Days, 1992, pages 58–60, 1992.[3] C. J. C. Burges. A tutorial on support vector machines for pattern recognition. Data Mining andKnowledge Discovery, 2:121–167, 1998.[4] P. Kropp, A. Kiewitt, H. Go¨bel, P. Vetter, and W. D. Gerber. Reliability and stability of contingentnegative variation. Appl Psychophysiol Biofeedback, 25:33–41, 2000.[5] B. Rockstroh, T. Elbert, A. Canavan, W. Lutzenberger, and N. Birbaumer. Slow Cortical Poten-tials and Behaviour. Urban and Schwarzenberg, 2nd edition, 1989.[6] G.J.M. Van Boxtel and K.B.E. Bo¨cker. Cortical measures of anticipation. Journal of Psychophys-iology, 18:61–76, 2004.[7] G. Vanacker, J. del. R. Milla´n, E. Lew, P. Ferrez, F. Gala´n, J. Philips, H. V. Brussel, and M. Nuttin.Context-based filtering for assisted brain-actuated wheelchair driving. Computational Intelligenceand Neuroscience, 2007:Article ID 25130, 2007.[8] W. G. Walter, R. Cooper, V. J. Aldridge, W. C. McCallum, and A. L. Winter. Contingent negativevariation: an electric sign of sensorimotor association and expectancy in the human brain. Nature,203:380–4, 1964.

Recognition of Anticipatory Behavior from Human EEG

Anticipation increases the efficiency of a daily task by partial advance activation of neural substrates involved in it. Single trial recognition of this activation can be exploited for a novel anticipation based Brain Computer Interface (BCI). In the current work we compare different methods for the recognition of Electroencephalogram (EEG) correlates of this activation on single trials as a first step towards building such a BCI. To do so, we recorded EEG from 9 subjects performing a classical Contingent Negative Variation (CNV) paradigm (usually reported for studying anticipatory behavior in neurophysiological experiments) with GO and NOGO conditions. We first compare classification accuracies with features such as Least Square fitting Line (LSFL) parameters and Least Square Fitting Polynomial (LSFP) coefficients using a Quadratic Discriminant Analysis (QDA) classifier. We then test the best features with complex classifiers such as Gaussian Mixture Models (GMMs) and Support Vector Machines (SVMs).LIDIAPCNBICNPIDIAP-RR 08-5

Recognition of Anticipatory Behavior from Human EEGG. Gangadhar1,2, R. Chavarriaga1, J. del. R. Milla´n1,21 IDIAP Research Institute, Rue Marconi 19, C.P. 592, CH-1920 Martigny, Switzerland.2Ecole Polytechnique Fe´de´rale de Lausanne, Lausanne, Switzerland.Gangadhar.Garipelli@idiap.chAbstractAnticipation increases the efficiency of a daily task by partial advance activation of neuralsubstrates involved in it. Single trial recognition of this activation can be exploited for anovel anticipation based Brain Computer Interface (BCI). In the current work we comparedifferent methods for the recognition of Electroencephalogram (EEG) correlates of this ac-tivation on single trials as a first step towards building such a BCI. To do so, we recordedEEG from 9 subjects performing a classical Contingent Negative Variation (CNV) paradigm(usually reported for studying anticipatory behavior in neurophysiological experiments) withGO and NOGO conditions. We first compare classification accuracies with features such asLeast Square fitting Line (LSFL) parameters and Least Square Fitting Polynomial (LSFP)coefficients using a Quadratic Discriminant Analysis (QDA) classifier. We then test the bestfeatures with complex classifiers such as Gaussian Mixture Models (GMMs) and SupportVector Machines (SVMs).1 IntroductionAnticipation is a process that not only depends on past and current states but also on futureexpectations. Without anticipation everyday cognitive tasks would become exclusively reactive.Conversely, this process increases the efficiency of daily tasks by partial advance activation ofthe neural substrates involved [1]. We hypothesize that the recognition of this activation canbe exploited for Brain Computer Interaction (BCI). For example, consider a scenario of a brain-actuated wheelchair [2] driving towards a table with breakfast lying among several other tables.Using the onboard sensors the intelligent controller in the wheelchair can detect the presenceof a table in front but it cannot decide by itself whether to dock or avoid. If the controlleris integrated with anticipation recognition algorithms, the user can issue the docking commandjust by anticipating the docking event to happen. Otherwise, the controller triggers the obstacleavoidance behavior.To the best of our knowledge, anticipation related potentials in human EEG are well studied inthe context of clinical science and functional neurophysiological studies [3] but not well exploredin the context of BCI, excepting one early attempt based on neurofeedback [4]. In the currentwork we compare different methods for the recognition of these potentials on single trials as a firststep towards the design of an anticipation-based BCI.To record these potentials we have considered a classical Contingent Negative Variation (CNV)paradigm [3] as an experimental procedure. A vast amount of literature describes the CNV po-tentials (the potentials recorded using CNV paradigm) as related to anticipation [3, 5, 6]. In thisparadigm a warning stimulus (S1) predicts the appearance of an imperative stimulus (S2) in a pre-dictable inter-stimulus-interval (ISI). A negative shift in the cortical activity with a centro-medialdistribution (under the vertex electrode, Cz) usually develops between S1 and S2 depending oncontingency of stimuli and task parameter relevance [5, 6]. This signal has been shown to bestable over several days and in different conditions (e.g., amount of sleep time) [5]. In addition,1one neurofeedback experiment suggests that humans are able to modulate its amplitude [4]. Thestability of this potential, and the human’s ability to modulate its amplitude, support the possi-bility of using this phenomenon for the design of anticipation-based BCI. To this end, it is firstnecessary to ascertain the feasibility of achieving reliable recognition of CNV potentials on singletrials; this is the goal of the present study. In the following sections we describe the experimentalsetup, along with classification techniques used in recognizing these potentials.2 Methods2.1 Experimental setupWe used the CNV paradigm with relevant (GO) and irrelevant (NOGO) conditions for simulatinganticipatory and non-anticipatory behaviors (e.g., the table with food corresponds to a relevantcondition). Fig. 1(a) and Fig. 1(b) describe the CNV paradigm used in the current study. TheEEG signals of nine male subjects (22-27 yrs.) were recorded in four consecutive sessions with50 trials each (equiprobable GO and NOGO trials in random order separated by an inter-trialinterval of 4± 4s).2.2 Data acquisition and preprocessingThe EEG signals were acquired for 9 subjects using 32 (subjects 4, 5 and 9) or 64 (remaining 6subjects) electrodes according to the 10/20 international system with a sampling rate of 512Hz.Raw EEG signals were first spatially filtered by using common average reference (CAR). Thesignals were then filtered using a low pass filter with cut off frequency of 15Hz and then the trialswere extracted and separated into GO and NOGO trials using S1 as the reference (i.e., onset of S1considered as at 0.0s) with [-1.0 5.0]s as total trial interval. Average voltage of the time windowfrom -1000ms to 0ms was used as a baseline.2.3 EEG grand averagesThe EEG grand averages at Cz electrode computed over subjects for GO and NOGO conditionsshow clear differences (see Fig. 1(c) for grand averages using 64 electrode set-up). Similar dif-ferences are observed in the case of 32 electrode set-up. From the topographic plots of averagescalp distribution we observed an increasing negativity under this electrode in GO condition and asmaller negativity in NOGO condition. An evoked response due to S1 is observed at this electrodearound 0.3s to 0.4s in both conditions. The potential at Cz during GO condition is composed ofan early peak around 1s and a late peak between 3.5s and 4.0s which is consistent with previousstudies [6]. Although clear differences are observed in grand averages, the use of these potentialsfor BCI imposes the challenge of recognizing them on single trial. The methods developed foraddressing these challenges are described in the next section.3 ClassificationAs the subjects were instructed to press a button on the arrival of S2 (at 4s) the recognitionmethods evaluated here are based on the EEG potentials up to 3.5s after the onset of S1 (0.0s)in order to avoid any movement preparation potential that could contaminate the recognition ofanticipation processes. In the scope of the current work we restrict to features computed from thepotential at Cz electrode alone (vCz(t), where t is time, t ∈ [0 Tmax] and Tmax = 3.5s).3.1 Feature selectionSince slope and peak negativity are usually reported as features of CNV potentials [1], we firsttest Least Square Fitting Line (LSFL) parameters with the help of a Quadratic Discriminant2(a) (b)(c)Figure 1: CNV experimental setup and ERP grand averages. (a) In the GO condition a warningstimulus (S1) with a green dot at time t = 0s is displayed and then an imperative stimulus (S2)with a red dot on the screen is presented with ISI of 4s. Subjects are instructed to anticipateand press a button as soon as S2 is presented. (b) To differentiate the NOGO condition fromthe GO condition S1 is replaced with a yellow dot. The subjects are instructed to do nothing forthis condition. (c) The grand averages of GO and NOGO trials for six subjects recorded with 64electrode configuration at Cz electrode. The circular figures are the topographic representation ofaverage scalp distribution at different time scales for GO (bottom) and NOGO (top) conditions.Analysis (QDA) classifier. We then compare higher order features such as Least Square Fit-ting Polynomial (LSFP) features computed as the coefficients of nth order LSFP (αi, wherei= 1,...n of v˜Cz = α0 + α1t1 + α2t2 + . . .+ αntn). Each trial is then described by feature vec-tor, x = [α0 α1 α2 . . . αn]T , where, x ∈ Rn+1. The best polynomial order for each subject ischosen by comparing training accuracies of classifiers calculated for n ∈ {2, 3, ..., 6} (the maximalorder for search is 6 due to the limited amount of training samples). The LSFL features areequivalent to the LSFP features with order one.3.2 ClassifiersWe compare classification of anticipation related potentials using the features described above withthe help of different classifiers described in the following paragraphs. Due to space limitations wegive only a very brief introduction of these classifiers.3Quadratic Discriminant Analysis (QDA). Similar to Linear Discriminant Analysis (LDA)classifier, the QDA classifiers assumes that the features are normally distributed and relaxes theassumption that the covariance of each class is identical. For the current problem, we first projectthe features onto a canonical space with the help of a projection matrix, that maximizes between-class variance and minimizes within-class variance, which can be obtained by maximizing Fisher’scriterion [7]. We then calculate QDA classifiers on the projected features.Gaussian Mixture Model (GMM). The GMM is a generative model widely used for clus-tering and classification applications. In the current study we first model each class distributionwith a separate GMM and using these models we build a classifier. The initial estimates of meansare obtained using a k-means algorithm and we then use the Expectation Maximization (EM)algorithm [7] for estimating the mean (µk), covariance (Σk) and mixing coefficients (pik) of eachGaussian component of the GMM. Due to limited number of training samples we reduce thenumber of free parameters to estimate by constraining the covariance matrix to be diagonal andsharing it among all the components. The best number of Gaussian components for each subjectis obtained by exhaustive search in the range {1, 2, ..., 4} based on training accuracies. Since thisclassifier suffers from the problem of local minima for complex data that are not well clustered,we built 100 different models with random initial Gaussian centers. The best model from all the100 models was then chosen using the training accuracy and considered for testing with test data.Support Vector Machine (SVM). SVMs are supervised learning methods that simultane-ously minimize empirical classification error and maximize the geometrical margin between twoclasses [8]. In the present study we report classification results based on linear kernel (SVM-linear)and Radial Basis Function kernel (SVM-RBF). The free parameters of the classifier are estimatedby 10-fold cross validation on training trials.4 ResultsTo assess the classification performance across sessions we did a 4-fold cross-validation study whereeach fold corresponds to a separate session. The results of this study are summarized in Table 1.We first did feature comparison with the help of QDA classifiers trained separately for each subject.We observed that the LSFP features outperform the LSFL features (Wilcoxon test p=0.01, overall the subjects and sessions), suggesting that these features describe the anticipation relatedpotential better than the LSFL features that are usually computed for the characterization of theCNV potential in neurophysiological studies [1, 4]. It is worth noting that no specific differencesare observed for EEG setup with 32 or 64 electrodes.Since LSFP features performed better, we tested them on the other complex classifiers suchas GMM and SVM-linear and SVM-RBF classifiers to compare with the performance of the QDAclassifier (see Table. 1). The classification accuracies of the QDA classifier are significantly betterthan the other three classifiers (Wilcoxon test, p=0.01 among all the subjects and sessions).Coming to individual subjects, the accuracies for subjects 6, 8 and 9 are close to random forall the methods. For subjects 1, 2, 3 and 5 the QDA classifier with LSFP features performedsignificantly better than the other classifiers (the best being subject 5, 75.86±6.45% ). On averagethe SVM classifier with linear kernel is the next best classifier. However, this classifier givesaccuracies above 65% only for one subject whereas the QDA classifier does so for 3 subjects.The SVM classifier with RBF kernel and GMM classifier perform worse compared to the QDAclassifier.Although the QDA classifier with LSFP features performs best compared to all the othermethods, the results of cross-validation show that the recognition method is not reliable enoughfor a BCI. Nevertheless, most subjects exhibit an increasing performance over sessions (we excludedthe subjects 6, 8 and 9 from this study due to the classification accuracies are close to randomin all sessions with all the classifiers). Fig. 2 illustrates this trend with the help of accuraciesaveraged over all the 6 subjects separately for each session. The accuracies in the 3rd and 4th4Table 1: Comparison of cross-validation accuracies with different features and classifiers.LSFL LSFPSubject QDA QDA GMM SVM-linear SVM-RBF1 62.15±8.62 66.14±6.78 58.02±9.44 58.02±5.69 58.52±7.922 64.61±13.17 66.04±6.90 64.55±5.68 64.55±6.31 54.37±5.443 47.57±5.56 59.91±10.97 46.87±1.82 46.87±7.37 45.93±7.684 50.50±5.74 58.00±7.48 61.00±5.29 61.00±12.48 51.00±3.835 68.83±8.95 75.86±6.45 66.31±7.36 66.31±6.60 46.73±4.116 53.64±8.40 53.09±9.55 43.54±4.00 43.54±6.43 52.44±3.037 44.64±3.01 54.77±11.93 57.04±7.20 57.04±10.03 53.75±4.448 53.82±9.39 51.28±4.25 44.01±9.09 44.01±9.36 47.11±6.409 51.00±8.08 51.00±5.29 51.00±9.59 51.00±12.70 53.00±8.25session are significantly higher ( 67.06 ± 12.30% and 66.77 ± 7.77) as compared to the first twosessions (58.60 ± 13.96 and 61.37 ± 5.61). Observing this trend, we argue that the features ofthis potential are becoming stable and well separable over time due to subjects adaptation to theexperimental paradigm. This suggests that, similar to BCI systems, a good training method isa key component for subjects to learn to provide stable EEG patterns. Proper training of thesubjects is likely to enhance the classification accuracies with the methods studied in the currentwork.1 2 3 44050607080Test sessionClassification accuracy (%)Figure 2: Average classification accuracies in different sessions using QDA classifier with LSFPfeatures for subjects 1-5 and 7.5 ConclusionsWe compared different classification techniques for the recognition of anticipation related potentialsfrom human EEG as a first step towards the design of a novel BCI. From the off-line studies on thesepotentials using the QDA classifier we observed that the LSFP features perform better than theLSFL features. This result suggests that LSFP features describe the anticipation related potentialbetter than the LSFL features, the latter being those usually computed in neurofeedback andneurophysiological studies for the characterization of CNV potentials [4, 1]. We also compared theclassification accuracies of LSFP features with QDA, GMM, SVM-linear and SVM-RBF classifiers.The 4-fold cross validation, with each fold corresponding to a separate session, showed that theQDA classifiers perform significantly better than the other classifiersIt is worth noting that none of the subjects considered in the current study had previousexperience with the CNV protocol. The systematic observation of the performance in each session5showed an increasing trend in classification accuracy for most (6 out of 9) subjects. We arguethat this trend is due to subjects’ adaptation to the experimental paradigm. However, for theremaining subjects the classification accuracy is at chance level which is most likely due to lack ofpractice (note that the CNV paradigm is a learned task and subjects need to practice for a fewsessions). As some subjects learn faster than others the classification accuracies also differ in thesame way. Moreover, as there is a learning component, the classifier calculated using the latersessions does not perform well for the early session, whereas the classifier calculated using earlysessions performs better for the later session (see Fig. 2).Proper training of the subjects is likely to enhance the performance of the recognition methodscompared in the current study. Moreover, an early study based on neurofeedback showing thatthe subjects were able modulate these potentials [4]. Based on this knowledge we hypothesizethat the closed loop implementation of the current recognition methods can improve the subject’straining yielding to more stable and well separable features. We consider this implementation asnext immediate step for further research. In addition, we will extend these methods to multi-electrode features in order to improve the classification accuracies. Also, fast recognition of thesepotentials is another crucial factor for building a reliable BCI application. We plan to achieve thisby extending the current methods to multi-classifier based recognition techniques in which eachclassifier looks at different temporal blocks of EEG and makes a decision as quickly as possible.6 AcknowledgementsThis work is supported by the EC-contract number BACS FP6-IST-027140 and the Swiss NationalScience Foundation NCCR “IM2”.References[1] G.J.M. Van Boxtel and K.B.E. Bo¨cker. Cortical measures of anticipation. Journal of Psy-chophysiology, 18:61–76, 2004.[2] G. Vanacker, J. del. R. Milla´n, E. Lew, P. Ferrez, F. Gala´n, J. Philips, H. V. Brussel, andM. Nuttin. Context-based filtering for assisted brain-actuated wheelchair driving. Computa-tional Intelligence and Neuroscience, 2007:Article ID 25130, 2007.[3] W. G. Walter, R. Cooper, V. J. Aldridge, W. C. McCallum, and A. L. Winter. Contingentnegative variation: an electric sign of sensorimotor association and expectancy in the humanbrain. Nature, 203:380–4, 1964.[4] L. Bozinovska, S. Bozinovski, and G. Stojanov. Electroexpectogram: Experimental design andalgorithms. In Proceedings IEEE Biomedical Engineering Days, 1992, pages 58–60, 1992.[5] P. Kropp, A. Kiewitt, H. Go¨bel, P. Vetter, and W. D. Gerber. Reliability and stability ofcontingent negative variation. Appl Psychophysiol Biofeedback, 25:33–41, 2000.[6] B. Rockstroh, T. Elbert, A. Canavan, W. Lutzenberger, and N. Birbaumer. Slow CorticalPotentials and Behaviour. Urban and Schwarzenberg, 2nd edition, 1989.[7] C. M. Bishop. Pattern Recognition and Machine Learning. Springer, 2006.[8] C. J. C. Burges. A tutorial on support vector machines for pattern recognition. Data Miningand Knowledge Discovery, 2:121–167, 1998.6

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Recognition of Anticipatory Behavior from Human EEG

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