Asynchronous multiclass mental tasks classification ... - IEEE Xplore

2014 IEEE Conference on Biomedical Engineering and Sciences, 8 - 10 December 2014, Miri, Sarawak, Malaysia

Asynchronous Multiclass Mental Tasks Classification through Very Fast Versatile Elliptic Basis Function Neural Network Mahyar Hamedi, EMBS Member, Sh-Hussain Salleh, IEEE Member, Iman Mohammad-Rezazadeh, Mehdi Astaraki, Alias Mohd Noor Abstract— Developing efficient and usable brain-computer interfaces (BCIs) requires well-designed trade-off between accuracy and computational time. This paper presents a very fast and accurate method to classify asynchronous brain signals from a multi-class mental tasks dataset using time-domain features. Five different statistical time-domain features were extracted to characterize various properties of three mental tasks electroencephalograms (EEGs). Versatile Elliptic Basis Function Neural Network (VEBFNN) was employed to classify single EEG features as well as multi-feature set. Discriminating power of single features was evaluated and compared by considering the classification accuracy and computational cost consumed during the training stage. Finally, the performance of the best single EEG feature was compared to the multifeature set. The results indicated the usefulness of Willison Amplitude EEG feature in classifying the different motor tasks as it provided the highest discrimination ratio. Classification results showed the high potential of VEBFNN by the average 89.78% accuracy and 0.21 seconds computation time obtained for its offline training. Moreover, VEBFNN outperformed the conventional support vector machine classifier in both terms of accuracy and speed.

I. INTRODUCTION In the past two decades, there has been growing interest for the biomedical and neural engineers as well as the neuroscientists to connect brain signals with computers/machines in order to provide a reliable and comfortable communication pathway for disabled people with external environment. Brain computer interface (BCI) systems aim to detect users’ thoughts and transform them into input commands that control devices like wheelchair and prosthesis hand [1-2]. Various BCI systems using EEG signals have been proposed among which imagination of Motor Imagery (MI)-based, imagination of different limb movements, is one of the most promising [3].

M. H. is with Center for Biomedical Engineering, Transportation Research Alliance, Universiti Teknologi Malaysia, 81310, Johor Bahru, Malaysia (phone: +6014-7730-290; e-mail: [email protected]). Sh-H. S. is with Center for Biomedical Engineering, Transportation Research Alliance, Universiti Teknologi Malaysia, 81310, Johor Bahru, Malaysia (e-mail: [email protected]). I. M-R. is with David Geffen School of Medicine, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles & Center for Mind and Brain, University of California, Davis, USA (e-mail: [email protected]). M. A. is with department of Biomedical Engineering, Science and Research Branch, Islamic Azad University Tehran, Iran (e-mail: [email protected] ). A. M. N. is with Center for Biomedical Engineering, Transportation Research Alliance, Universiti Teknologi Malaysia, 81310, Johor Bahru, Malaysia (e-mail: [email protected]).

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Imagination of different motor tasks like right/left hand movements generate different EEG features in the brain hemispheres corresponding to specific sensorimotor areas. Such electrical brain activities produced by underlying groups of neurons can be detected non-invasively by surface electrodes. To classify commands from these electrophysiological time-varying signals several stages of processing are necessary. Feature extraction and classification play the most dominant roles for designing BCIs as they directly influence the performance indices of the designed system. Since BCIs have been mainly proposed to be used for online applications, some considerations need to be taken into account like a reliable trade-off between classification accuracy and computational complexity. Although many robust techniques have been introduced for feature extraction and classification of MI signals, they have not considered the trade-off criteria and could not efficiently tackle the observed problems in real-time BCI applications. In terms of feature extraction, more studies have focused on the extraction of spectral, time-frequency, and spatial features like Quantification of Event-Related Synchronization/ Desynchronization (ERS/ERD) phenomenon [4]; Morlet Wavelet transform [5]; Empirical Mode Decomposition (EMD) [6]; Hilbert-Huang Transform (HHT) [7]; Short-Time Fourier Transform (STFT) [8]; Principal Component Analysis [9]; Independent Component Analysis (ICA) [7]; Common Spatial Pattern (CSP) [8]. These features are computationally expensive as they need different levels of transformation which lead to longer processing time. On the other hand, classification problem has been addressed by numbers of linear and non-linear algorithms with different complexity and efficiency such as SVM [9], [10], K-Nearest Neighbor (KNN) [11], and Linear Discriminant Analysis (LDA) [12]. Even though high accuracy might have been achieved by the mentioned methods, computational cost has neither been investigated nor reported vividly. Recently, the effectiveness of timedomain (TD) features for characterizing the MI movements for BCI systems has been investigated [9], [13-14] and it is reported that not only these features are very simple and easy to compute but also they could perfectly represent the discriminative information of different MI EEGs and lead to high degree of classification accuracy. Although very few studies extracted this type of features for MI-based BCIs, it has been shown that they deliver different classification performance when classified by various techniques. In this paper, by considering the TD feature extraction approach we offered a new methodology to provide a reliable trade-off between computational cost and accuracy when EEG signals were recorded asynchronously. Five different

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time-domain features namely, Mean Absolute Value (MAV), Maximum Peak Value (MPV), Root Mean Square (RMS), Simple Square Integral (SSI) and Willison Amplitude (WAMP) were extracted from EEGs to examine diverse characteristics of three MI signals. To classify these features we proposed using a very fast versatile elliptic basis function neural network (VEBFNN). Discriminating power of single features was evaluated and compared through the classification accuracy and computational cost consumed during the training stage. Moreover, the performance of the best single EEG feature was compared to a multi-feature set comprised of all existing features. The classification performance of VEBFNN was assessed and compared with the widely used method, SVM, based on the considered performance indices. II. VEBFNN VEBFNN, an extension of radial basis function (RBF) and elliptical basis function (EBF) neural networks, was introduced by Saichon et al., [15]. This method was developed to tackle the following observed shortcomings in traditional neural networks: Gaussian kernel function as the key basis function of most learning algorithms is strictly dependent on center selection which requires crucial parameters estimation [16]; the fix structure of RBF and EBF during training is not suitable in sequential learning; all training techniques need previously trained data and new incoming data; many epochs are required in learning procedure due to the gradient and optimization methods; in EBF, Ellipsoidal Basis Function cannot rotate to cover the data like the Gaussian function. VEBFNN is designed by a hyper-ellipsoidal function which is able to rotate and cover the dataset during learning procedure. The significant benefit of this method is that only one epoch is enough to train the data and once a datum is learnt it can be discarded and there is no need to take the previous trained data into account. This makes VEBFNN very fast and reasonable for real-time applications [15]. This network consists of three layers: input layer where the number of neurons is equal to the dimension of the input dataset; output layer in which the number of neurons is equal to the number of classes in the training dataset; and the hidden layer for which the number of neurons is not defined in advance and it is formed during the training process. The output of the kth neuron in the hidden layer for each given input feature vector X is computed by: n

ψ k (X ) = ∑ i =1

(( X − C ) T u i ) 2 −1 ai2

(1)

Where ai is the width of the ith axis of the VEBF and it is centered at the vector C = [c1 , c2 ,..., cn ]T and rotates along with orthonormal basis {u1 ,u 2 ,..., un } to cover all nearby data without increasing the radius of neuron. More details of VEBFNN including the parameters adjustment, training stages, evaluation and comparison with some conventional classifiers on different datasets can be found in [15].

III. METHODOLOGY In the beginning of this section, the procedure of EEG data collection including the system setup, electrode placement, recording protocol and EEG preprocessing is presented. Then, extraction of different MI EEG features is explained. And finally, classification of extracted features is described. A. Motor Imagery Data Acquisition In this experiment scalp EEG signals were recorded from ten healthy subjects who were right-handed and in the age range of 25-34 using g.tec device and three EEG channels C3, Cz and C4 (based on the international 10/20 system) with a 512 Hz sampling rate. Reference electrode was placed on the left mastoid, behind the ear and the ground electrode was located at FPz, near the forehead. EEGs were band-pass filtered (0.5 and 30 Hz) so as to envelope the significant spectrum of MI tasks, and a notch filter was employed to eliminate the power line inference noises (50 Hz). The subjects sat on a comfortable armchair with closed eyes for 2 minutes in order to relax. Noncue-based (asynchronous) recording scheme was considered in this study while subjects were asked to continuously imagine raising the left and right hands, and moving the tongue (any direction). The movements were performed in three separate runs (each run lasted one minute) with intervals of 5 minutes rest. Besides, the subjects executed the MI movements without any feedback. Filtered EEGs were segmented into non-overlapped windows with 256 milliseconds length and then got prepared for feature extraction. B. Feature Extraction Recorded EEG signals are generally large in dimension and are contaminated with redundant and useless information. Therefore, feature extraction is a necessary step in pattern recognition-based EEG signal analysis which aims to decrease the dataset and eliminate non-informative data. Such transformation requires precise techniques to highlight the appropriate information of each MI in order to provide highly discriminative features and convey a better system performance. There are numerous methods with variety of complexity and efficiency in diverse domains which represent different MI EEG signals characteristic. Timedomain features are easy to compute as they are estimated from EEG signal amplitude and they need no transformation. The usefulness of these features has been also proven in processing other biosignals (e.g. [17]). The discriminating power of such features was investigated for MI EEGs [9], [13-14]; however, they may perform differently when using other classifiers. In this study, we computed and extracted five more efficient time-domain features Mean MAV, MPV, RMS, SSI and WAMP from the segmented EEGs according to the equations in Table 1. In order to make more separable feature vectors, log transform was employed on extracted features to spread the concentrated features when considering the highly scattered features. Moreover, to construct the NN, each feature set was shuffled and divided into 70% and 30% data sets for training and testing respectively.

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TABLE I.

MATHEMATICAL DEFINITION OF CONSIDERED FEATURES, K IS THE CURRENT SEGMENT, N IS THE LENGTH OF THE SEGMENT, XI IS THE CURRENT POINT OF SIGNAL AND I IS THE INDEX OF THE CURRENT POINT

100 95 90

RMS k =

N ∑i =1

1

N

∑i =1 xi2 N

N

xi

MPVk = max xi

85 Accuracy (%)

MAVk = 1

( )

SSI k = ∑i =1 x N

2 i

75 70 65

N −1 WAMPK = ∑ i =1 f ( x ), f ( x) = ⎧⎨1 xi − xi+1 > T

Average accuracy over all subjects 60

otherwise

55 MPV

C. Classification Extracted features need to be classified into distinctive classes. Since the success of MI-based BCIs depends directly on classification accuracy, a proper classifier should be employed to yield high performance. In addition, it should be able to classify the new incoming patterns very fast in realtime applications. In this paper, VEBFNN was proposed to classify the three MI movements for the first time since it technically meets the above criteria. The efficiency of VEBFNN has been validated on other different datasets and applications [15], [18]. As it is a three layer neural network (like RBF), input and output layers were comprised of three neurons (equal to the dimension of the feature vector and the number of classes respectively). Since neurons of the hidden layer were formed during the training procedure, they were not predefined. This algorithm includes five parameters from which only width vector of the first neuron needs to be initialized. Since the final performance of this classifier depends directly on this parameter, a wide range of values were examined while a five-fold random cross-validation was employed to evaluate the parameters. Finally, a sphere with a radius of 0.1 was picked. IV. RESULTS AND DISCUSSION In this section the results achieved from the experiments are presented and discussed. At first, discriminating power of features was evaluated and compared through classification accuracy and training time which highlight the final performance of the test data sets and the computational load during the training stage respectively. Then, internal performance of VEBFNN was visualized by means of confusion matrix when using the best feature. Finally, the performance of VEBFNN was compared to SVM. Fig. 1 demonstrates the classification accuracy averaged over all subjects when using different features. It can be seen that SSI, MAV and RMS provided almost identical discrimination ratio. MPV with 73.14%±9.12 and WAMP with 89.78%±6.59 delivered the lowest and the highest classification accuracy. Analytical comparison provided in box-plots indicates that the interquartile ranges for all features except for WAMP were reasonably similar and they delivered the same degree of dispersion. WAMP was shaped in a short box and it shows that the classification accuracy alteration over different subjects was less than other features. Moreover, symmetric boxes of MAV and WAMP point out that the accuracies achieved over different subjects were evenly split at the median. The outliers of WAMP and MPV in this figure indicate that the classification accuracy achieved for two subjects were far from the median since they were not located in the interquartile range.

SSI

MAV Feature

RMS

WAMP

Figure 1. Statistical comparison of features by considering classification accuracy averaged over all subjects. Average training time over all subjects 0.4

0.35 Time (second)

⎩0

80

0.3

0.25

0.2

0.15

MPV

SSI

MAV Feature

RMS

WAMP

Figure 2. Statistical comparison of features by considering training time averaged over all subjects.

Fig. 2 illustrates the time consumed during the training stage when using each type of feature. Obviously, WAMP required very short time (0.21±0.05 seconds) to be trained. That is because this feature was much more discriminative respect to others. On the contrary, MPV needed the most training time (0.34±0.04 seconds). VEBFNN consumed equal time to train SSI and MAV which indicated a similar distribution for these features. The considerable point about the box-plots in Fig. 2 is the high stability of SSI respect to different subjects since the spread of training times were limited in a very short range in spite of WAMP which was formed in a long box. Furthermore, MPV and WAMP boxes show the evenly split of the consumed training time over different subjects at the median. The outliers of SSI, RMS, and MAV represent that four values were far from their medians. According to the results in Fig. 1 and 2, WAMP was selected as the most efficient single feature as it provided the highest accuracy and lowest training time among all. In this study the discriminative power of multi-feature set was also evaluated in such a way that all single features were concatenated and a 15-dimensional feature vector was made. Once more, VEBFNN was applied to classify this feature set for each subject. Averaged results showed that classification accuracy was negligibly improved (about 1%) compared to the one achieved by WAMP. However, the computational cost was significantly increased to 2.17±0.17. Accordingly, single feature WAMP was preferred since it resulted in a better trade-off between accuracy and computational cost. Table 2 presents the confusion matrix to visualize the classification performance by VEBFNN when WAMP was used. This table shows how the features were classified and misclassified. It indicates that left hand was misclassified in place of right hand and tongue almost equally with the rates of about 6.33% and 6.67% respectively. Besides, right hand was more misclassified in place of left hand (7.33%) rather

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TABLE II.

CONFUSION MATRIX WHILE AVERAGED OVER ALL SUBJECTS

WAMP FEATURE WAS USED

Predicted Class (%)

Actual Class

Left hand

Right hand

Tongue

Left hand

87

6.33

6.67

Right hand

7.33

88.67

4

Tongue

3.33

3

93.67

than tongue. The tongue misclassification rate was about 6% which was split almost identically between left and right hand MIs. It is also clear that tongue was the most distinguishable MI movement respect to others while 93.67% features were classified correctly. In the last experiment, the performance of VEBFNN was compared with the widely used classifier, SVM. In order to report a fair comparison, the best SVM model was implemented in which Polynomial kernel was employed and the optimum parameters for each subject were selected among a range of values by a five-fold random crossvalidation scheme. This comparison was carried out through obtained classification accuracy and training time while WAMP was fed. As shown in Fig. 3, VEBFNN outperformed SVM since it achieved higher accuracy (~1%) and consumed lower time (~0.29 seconds) during the training. Furthermore, box-plots revealed that VEBFNN was more stable since the accuracy variations over all subjects were formed in a shorter box. The results of this study support the idea of using timedomain features for discriminating the multi-class MI movements. Significantly, the effectiveness of WAMP as suggested in [9] was again approved here. Finally, the performance of VEBFNN indicated its robustness for classification of motor tasks in very short time. V. CONCLUSION In this study, the efficiency of five single time-domain features as well as a multi-feature set was investigated in classification of three-class MI movements. Additionally, the robustness of VEBFNN classifier was examined as a new approach in MI. It was shown that various time-domain features led to different discrimination ratios. Among all WAMP provided higher discrimination ratio for MI classification. VEBFNN revealed its high potential to classify the motor tasks since it reached a reliable trade-off

between the accuracy and speed. Besides, it outperformed SVM by obtaining higher accuracy and consuming less time for training the features. Although a new procedure for classification of asynchronous MI movements was proposed in this study, effective filtering and artifact removal methods which can enhance the performance indices should be added in future works. ACKNOWLEDGMENT This research project is supported by Center of Biomedical Engineering (CBE), Transport Research Alliance, Universiti Teknologi Malaysia research university grant (R.J130000.7809.4F434) and funded by Ministry of Higher Education (MOHE). REFERENCES [1] [2]

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100

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SVM

0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

Training Time (Sec)

Accuracy (%)

90

Feature

[13]

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Figure 3. Comparison of VEBFNN and SVM in terms of classification accuracy and training time averaged over all subjects.

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