Evidential Multi-Band Common Spatial Pattern in Brain Computer Interface

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1 Evidential Multi-Band Common Spatial Pattern in Brain Computer Interface Mariam Rostami Department of Biomedical Engineering Amirkabir University of Technology Tehran, Iran Mohammad Hassan Moradi Biological Signal Processing Laboratory Amirkabir University of Technology Tehran, Iran Abstract In electroencephalography (EEG)-based brain computer interfaces (BCI) one way to extract discriminative command-related patterns is to use common spatial pattern (CSP). Speech-imagery BCIs on the other hand, are a more recent type of BCIs in which imagination of a vowel, sound or word is detected from EEG measurements. Since the performance of CSP highly depends on the spectral filters which are previously applied to the signal, finding the appropriate frequency band seems to be a major concern. In order to overcome this problem, the novel evidential multi-band common spatial pattern (EMBCSP) is proposed. In EMBCSP, features are extracted using CSP after multiple band-pass filtering. The preliminary classification results are later given to a fusion system and then the features are labeled regarding the larger combined probability value. The performance of this method has been assessed using speech-imagery-based EEG measurements and has also been compared with some prevalent methods. The results suggest the outperforming of EMBCSP with accuracies between 75 to percent and give rise to opportunities to apply such a method to other common types of BCIs. Keywords-Brain-computer interface; speech imagery; electroencephalogram; common spatial pattern I. INTRODUCTION Brain computer interface (BCI) is designed to bypass efferent pathways. It replaces them by an artificial system of communication and control [1]. Applications of EEG-based BCIs mainly include communication (e.g. environmental control), mobility (e.g. wheelchair control), cognitive diagnostics (e.g. coma detection), rehabilitation, prosthetics and recreation (e.g. virtual reality) [2]. Studies have shown that mental imagination of hand/foot/tongue movement can be detected from EEG measurements [1]. Nevertheless, the number of imagination classes to be found in motor imagery tasks is limited and the maximum number of classes up to this point is four [3]. Another approach in motor imagery based BCIs is imagination of lips movement during rehearsal of a vowel which has been proposed in [4] using /a/ and /u/ vowels. Later in 2013, Wang et al. introduced characters speech imagery which involved mental reading of two Chinese words with different shapes and pronunciations [5]. It has been demonstrated that motor cortex activation occurs during mental reading of a vowel [6, 7]. In addition, some other studies have shown that tasks involving phonological processing in brain cause activation in some regions of inferior frontal gyrus [8]. In our study we intend to exploit both movement-related activations in motor area and phonological-processing in regions of inferior prefrontal cortex. Therefore, subjects are asked to both mentally read and imagine lips shape while reading a specific vowel which engages phonological and motor activations respectively. Moreover, event-related synchronization and desynchronization (ERS/ERD) arise from an external/internal event and are defined as a change in the ongoing EEG rhythms [9]. Since theses phenomena are highly frequency band specific, the band-pass filter used in our algorithm should be carefully configured to enable the recognition of ERD/ERS. On the other hand, the efficacy of common spatial pattern (CSP) as a tool for maximizing between-class separability [10] highly depends on the frequency band of the band-pass filter. There are usually two ways for assigning bandwidth of the filter including manual adjustment and combining/optimizing the outcomes corresponding to various filter bands. For the latter approach, the key methods proposed so far include subband common spatial pattern (SBCSP) [11], filter-bank common spatial pattern (FBCSP) [12] and baysian spatiospectral filter optimization (BSSFO) [13]. In the first two methods, the main filter bandwidth has been segmented into non-overlapping bands and the features resulting from each band are combined at the end, whereas in the last method (BSSFO) the optimization algorithm does not necessarily provide large confidence values for the optimal bandwidths. In our study, we endeavor to add uncertainty via applying several overlapping filters with different bandwidths to EEG signal. In the last stage, an evidence-theory-based method is used to merge corresponding decisions into one final label for each trial. In section II, data characteristics and the experimental paradigm have been described. Section III introduces EMBCSP in detail. Experimental results are then indicated and discussed in section IV. Section V concludes this paper.

2 II. METHODS A. Data acquisition Five subjects, 3 males and 2 females with the ages from 23 to 33 participated in this study. All subjects were right handed based on Edinburgh Inventory assessment [14] and had not attended similar experiments before. The paradigm was explained to each subject at the beginning of the session. None of the participants had any neurological or psychiatric disorders or serious medical conditions. Data were recorded using 16 active electrodes placed almost symmetrically (Fig. 1) on either sides of the head. Speech processing is mainly performed by the left hemisphere covering Broca s and Wernicke s area. However, in contrast to some studies in which the left hemisphere has been reported to be dominant in language functions [15], some other studies suggest that for a relatively small group of right/left-handed subjects language functions may occur in the right hemisphere [16]. Nonetheless, it has been shown in [5] that the electrode montage covering only the left side of the head resulted in higher discrimination accuracies. Moreover, the most significant spatial patterns with higher weights were almost located on the left in the same study. Thus, we preferred to place electrodes with a rather symmetrical montage with one exception on the left due to a higher probability of functions happening on the left side of the brain and also the limited number of electrodes we intended to use. Electrodes were referenced to the left earlobe and grounded to the forehead. Sampling frequency was 512 Hz and no filtering was applied during data recording. B. Experimental paradigm The training paradigm is demonstrated in Fig. 2. In each trial, the subject is asked to concentrate and be ready while a fixed cross is displayed on the screen for 2~3 s. After that, the cue appears on the screen for 1s. The cue is one of two vowels (/ӕ /, /u: /) or control which results in three separate classes. In the next 4 seconds, the subject keeps reading the corresponding vowel and simultaneously imagines the shape of his/her lips pronouncing it without making any movement or sound. In case of control, the subject is not expected to perform any special mental task although no movement is permitted. Finally, a 3s period is specified for rest while displaying an asterisk. The subject is asked not to blink during trials except for the rest time. In each session 4 runs are held each of which includes 9 times of every cue displayed in a random order (equivalently 27 trial in each run). A rest for 5 minutes is considered between every two runs. III. EVIDENTIAL MULTI-BAND COMMON SPATIAL PATTERN (EMBCSP) The proposed Evidential Multi-Band Common Spatial Pattern is illustrated in Fig. 3. It consists of five main stages which are applied on preprocessed data. EEG signals are first zero-phase band-pass filtered. In previous studies, the efficient bandwidth of filter for speech imagery EEG has been chosen as 6-30 Hz [5]. Therefore, the bandwidth of each filter in our study is set so that it includes the frequency range of 6-30 Hz. Figure 1. Electrode montage of the EEG setup. Figure 2. Timing of the speech-imagery task. Time segments of Imagine period are used in CSP. Figure 3. Architecture of the proposed Evidential Multi Band Common Spatial Pattern (EMBCSP) approach. Lower bound varies from 4 to 15 Hz while the upper bound alters from 18 to 30 Hz with an increment of 0.5 Hz. In the second stage, the binary-class version of common spatial pattern (CSP) as a tool for spatially filtering data is performed on spectrally filtered signals of each band. Extracted features corresponding to each band are then used to train a support vector machine (SVM) classifier. Each classifier results in a model based on which probabilities of every trial (feature vector) belonging to each class is computed. Having 2*M probabilities for every feature, Dempster- Shafer rule of combination is utilized in order to combine probabilities of each band. Finally, a simple maximum on 2 combined probabilities (each relating to one class) assigns each feature s label. A. Common spatial pattern In the second stage of EMBCSP, features are extracted using binary CSP which is performed based on joint diagonalization of two covariance matrices. The cost function for maximizing separability between two classes is defined as (1), where w is the vector composing the projection matrix W

3 used for mapping time series (X 1, X 2) into an m dimensional filtered series of signals (m is determined to be 4 in our study). w = argmax w ( wx 1 2 / wx 2 2 ) (1) The solution of this problem involves eigendecomposition of whitened covariance matrices of the two classes. Once W is found, the corresponding decomposition of the trial E is obtained by (2). Z = WE (2) The spatially filtered trials are converted into features using (3) which are utilized for classification in further steps. X p = log (var (Z p)) (3) B. Classification of extracted features In stages three to six of Fig. 3, extracted features are classified with respect to probabilities that are computed using an SVM. Training Features relating to each band are given to a separate SVM. M models are trained based on M sets of training features. Linear SVM implementation in Matlab Bioinformatics toolbox was used in this paper. For each test feature, instead of directly evaluating its label based on the location of our data in the feature space with respect to the separating hyper-plane, we computed the probability of test feature belonging to each class. The method of extracting these probabilities is Platt Scaling [17], where a sigmoid function is fit to SVM scores (representing distance between feature and main hyper-plane) as (4) where p(c i f) is the probability of the test feature with SVM score f belonging to class i. p (c i f) = 1/(1+exp (Af+B)) (4) In (4), parameters A and B are fit using maximum likelihood estimation from the training set of features and labels. After computing all probabilities, they get normalized if necessary so that the summation over classes equals to one. In our study with 2 classes, we obtain 2 probabilities for every feature representing the chance of the feature matching the corresponding class. Utilizing the described method, for each trial there are 2*M probabilities. In order to assign each trial s label, Dempster- Shafer rule of combination is used [18]. Dempster-Shafer theory of evidence is widely used for modelling uncertainty. The frame of discernment Θ in our study is defined as Θ= {1, 2} including mutually exclusive and exhaustive propositional hypotheses. Basic probability assignments (bpa) are also defined based upon the probabilities computed previously since they satisfy the two conditions given in (5) where Ø is an empty set and A is any subset of Θ. m(ø) = 0, A Θ m(a) = 1 (5) The un-normalized Dempster-Shafer rule of combination integrates two mass functions (m1 and m2) into one using (6). m(c) = A B=C m 1(A) m 2(B) (6) In preset study, the probability of the feature matching the class c i is determined through combining all M probabilities computed from M bands. Assuming every band as an expert, we have M experts each with two votes (in binary classification). Using Dempster-Shafer combination rule, experts opinions are combined into one opinion including 2 probabilities. In the last stage of the algorithm, the class corresponding to the maximum value over two classes is selected as the winner. IV. RESULTS AND DISCUSSION For single trial analysis of each subject, we use the signal within a temporal window of 1 second long and permute this window on the whole 4 seconds of the cue with an increment of 0.5 second in order to find the optimal 1 second time interval to use for feature extraction. In this paper, CSP, BSSFO and EMBCSP are evaluated for different time intervals. Test accuracies corresponding to the time window with the highest training accuracy are reported as the final result. Therefore, the optimal temporal window is distinguished based on the highest accuracy obtained by applying the methods on training data and signal within such a time interval is then used for feature extraction. Figure 4. Classification accuracies for imagination of /ӕ/ vs. /u: / with different methods for five healthy subjects.

4 Figure 5. Classification accuracies for imagination of /ӕ/ vs. control with different methods for five healthy subjects. Figure 6. Classification accuracies for imagination of /u: / vs. control with different methods for five healthy subjects. This strategy provides an opportunity to compare the performance of different methods since each of them utilizes an appropriate segment of the signal which might be different from the one used by the other algorithms. Leave-one-out (LOO) cross-validation technique is used to validate the model. In LOO, all features but one are exploited to train a model and the remaining feature is validated based on the trained model. Since no random permutation is required in LOO, validation process is only carried out once. Features obtained from CSP algorithm are classified using three types of classifiers including LDA, linear SVM and SVM with radial basis function kernel (with gamma 1). In BSSFO, the initial frequency band is set to 6-30 Hz and 10 particles are used. Fig. 4-6 demonstrate the accuracy rates for each subject and method. According to the accuracies in Fig. 4-6, EMBCSP outperforms other methods in most cases with an improvement in the range of 2 to 10 percent. CSP method with LDA or linear SVM resulted in almost the same accuracies due to their linearly discriminative properties whereas the outcome of RBF-SVM often did not improve mostly because of an improper parameter (gamma) selection which is not within the scope of this study. BSSFO improved the results in some subjects compared to the results of CSP, although its parameters needed to be well regulated in order to achieve higher accuracies. For subject 1, EMBCSP results in accuracies of 5 to 7 percent higher compared to the best result among other conventional methods. Classification results of the proposed method in subjects 4 and 5 are also higher than the other methods with an improvement of 1 to 10 percent. For subject 2, not much improvement of EMBCSP occurred especially for discriminating between /ӕ / vs. control and /u: / vs. control. Since BCIs are usually designed independently for subjects, such a failure in improving the discriminative properties between some classes should not be considered as a major issue. In fact, in case of this specific subject other methods of feature extraction or classification can be used without affecting the superiority of the suggested method over the other examined algorithms.

5 Worth mentioning, the average results (over subjects) shown in Table I suggest the prominence of EMBCSP over the other methods. Although classification accuracies of two vowels are usually expected to be smaller than that of a vowel vs. control, such an event did not happen regarding the average results over subjects. Nonetheless, in most subjects the accuracies of vowels against control were larger than those of vowels against each other suggesting that the discrimination between two separate imaginations is harder than the discrimination of one imagination against control. V. CONCLUSION This paper proposed a novel evidential method utilizing multiple frequency bands each covering a potentially efficient frequency range for the optimal feature generation and classification. Speech imagery data were used to test the performance of the method. The fundamental idea of this framework is the uncertainty concept which has been indirectly applied in the algorithm to combine different classification results based on features which have been filtered with various bands. The method has been compared with some typical algorithms of the literature addressing the frequency selection problem and it has resulted in superior performance. The variations among different methods results can be partly justified by their possible degrees of freedom. In most algorithms, different user-defined parameters which are manually selected can result in different outcomes. Although our method was implemented on all subjects using the same set of frequency bands, it resulted in higher accuracies than common methods due to an inherent voting procedure. The robustness of this approach makes it possible to be exploited in other signal processing applications where no prior information is existent in advance for assigning the proper initial parameters. Such method has the potential to be used in other types of oscillation-based EEG measurement approaches including motor imagery. TABLE I. AVERAGE ACCURACY VALUES FOR DIFFERENT METHODS AND CLASSES (%) Classes /ӕ / vs. /u: / /ӕ / vs. cont. /u: / vs. cont. CSP LDA LSVM RBFSVM ± ± ± ± ± ± ± ± ±3.53 BSSFO ± ± ±3.52 EMBCSP ± ± ±7.28 REFERENCES [1] J. R. Wolpaw, N. Birbaumer, D. J. McFarland, G. Pfurtscheller, and T. M. Vaughan, "Brain-computer interfaces for communication and control,"clin. Neurophysiol., vol. 113, no. 6, pp , [2] D. S. Tan and A. Nijholt, Brain-Computer Interfaces: Applying our Minds to Human-Computer Interaction: Springer, 2010, pp [3] M. Naeem, C. Brunner, R. Leeb, B. Graimann, and G. Pfurtscheller, "Seperability of four-class motor imagery data using independent components analysis," Journal of neural engineering, vol. 3, p. 208, [4] C. S. DaSalla, H. Kambara, M. Sato, and Y. 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