classification of mental tasks using stockwell transform

Computers and Electrical Engineering xxx (2014) xxx–xxx

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Computers and Electrical Engineering

journal homepage: www.elsevier .com/ locate/compeleceng

Classification of mental tasks using stockwell transform q

http://dx.doi.org/10.1016/j.compeleceng.2014.01.0100045-7906/� 2014 Elsevier Ltd. All rights reserved.

q Reviews processed and approved for publication by Editor-in-Chief Dr. Manu Malek.⇑ Corresponding author. Tel.: +60 104646023.

E-mail address: [email protected] (V. Vijean).

Please cite this article in press as: Hariharan M et al. Classification of mental tasks using stockwell transform. Comput Electr Enghttp://dx.doi.org/10.1016/j.compeleceng.2014.01.010

M. Hariharan a, Vikneswaran Vijean a,⇑, R. Sindhu b, P. Divakar a, A. Saidatul a, Sazali Yaacob a

a School of Mechatronic Engineering, University Malaysia Perlis, 02600 Perlis, Malaysiab School of Microelectronic Engineering, University Malaysia Perlis, 02600 Perlis, Malaysia

a r t i c l e i n f o

Article history:Available online xxxx

a b s t r a c t

In recent years, various physiological signal based rehabilitation systems have been devel-oped for the physically disabled in which electroencephalographic (EEG) signal is oneamong them. The efficiency of such a system depends upon the signal processing and clas-sification algorithms. In order to develop an EEG based rehabilitation or assistive system, itis necessary to develop an effective EEG signal processing algorithm. This paper proposesStockwell transform (ST) based analysis of EEG dynamics during different mental tasks.EEG signals from Keirn and Aunon database were used in this study. Three classifiers wereemployed such as k-means nearest neighborhood (kNN), linear discriminant analysis (LDA)and support vector machine (SVM) to test the strength of the proposed features. Ten-foldcross validation method was used to demonstrate the consistency of the classificationresults. Using the proposed method, an average accuracy ranging between 84.72% and98.95% was achieved for multi-class problems (five mental tasks).

� 2014 Elsevier Ltd. All rights reserved.

1. Introduction

Patients with Amyotrophic lateral sclerosis disease might have permanently lost all the voluntary muscle controls. Suchpeople can rely only on their cognitive abilities to interact with others. New technology such as the physiological signalbased rehabilitation system offers a glimmer of hope for them. Physiological signal based rehabilitation system has seena vast development in the recent years. EEG signals are recorded non-invasively by placing the EEG electrodes over the scalp.The recorded signals illustrate the responses of brain due to the various tasks performed [1]. Various researchers have stud-ied on the EEG signals captured during different mental tasks through different methods of analysis namely time domain,frequency domain and time–frequency domain analysis. These studies were useful for the application of enabling aphysically disabled to communicate with the environment. In this work, ST based analysis of EEG dynamics was proposedto classify the EEG signals. EEG signals from Keirn and Aunon [1] database were used for the analysis which consists of EEGrecordings of seven subjects performing five distinct mental tasks namely baseline, multiplication, letter composition,rotation and counting. The short description of such mental tasks is given below:

(i) Baseline task (B): subjects were asked to relax as much as possible while the EEG recording was done.(ii) Multiplication (M): subjects were asked to solve non-trivial arithmetic problems such as 49 times 78 without vocalizing

or make any overt movements.(iii) Letter task (L): subjects were asked to mentally compose a letter without vocalizing.

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(iv) Rotation task (R): subjects were given a diagram of a 3-dimensional block to study and later, they were asked to imag-ine that object being rotating in an axis.

(v) Counting task (C): subjects were required to imagine a blackboard and to visualize numbers being written sequentially.

The subjects were seated in a sound controlled booth with dim lighting. EEG signals were recorded using a non-invasiveelectrode cap from six different electrode positions C3, C4, P3, P4, O1 and O2 according to the 10–20 systems. EEG signalswere recorded at 250 Hz, for a period of 10 s (one trial). Five trails of all the seven subjects were utilized equally in this work.The objective of this work was to distinguish two-class (pair wise) and multi-class (combinations of three, four and five men-tal tasks) classification of EEG signals of five mental tasks. Firstly, classification experiments were conducted by using tendifferent pair wise combinations of mental tasks (BC, BL, BM, BR, CL, CM, CR, LM, LR, and MR). Secondly, classification exper-iments of ten different three-class combinations of mental tasks (BCL, BCM, BCR, BLM, BLR, BMR, CLM, CLR, CMR, and LMR)were performed. Next, five different four-class combinations of mental tasks were considered (BCLM, BCLR, BCMR, BLMR,and CLMR) and classification experiments were carried out. Finally, classification experiment was performed using all thefive mental tasks (BCLMR).

The paper is organized as follows: Section 2 gives a brief summary of the previous works. Section 3 describes the featureextraction using ST and classifiers used. Section 4 provides the results and discussion. Section 5 presents the conclusion.

2. Previous works

In the literature, several works could be found related to the classification of mental tasks. Researchers have used stan-dard EEG mental tasks database or their own database. In this work, Keirn and Aunon database was used to gauge the pro-posed method. Summary of significant works using Keirn and Aunon database were explained below:

In [2], researchers have employed five different classifiers namely neural network, Bayesian graphical network, Bayesianquadratic classifier, Hidden Markov model and Fisher linear classifier to distinguish the pair wise combinations of mental tasks.Autoregressive coefficient features were used to train the networks. Four different subjects (subjects 1, 3, 5 and 6) were usedand they have achieved a highest average accuracy of 94.07%, 89.22%, 86.58%, and 92.49% for subjects 1, 3, 5 and 6 respectively.

Palaniappan [3] has proposed power and asymmetry ratios from delta, theta, alpha, beta and gamma bands as features inclassifying pair wise combination of mental tasks from four subjects. Elman neural network (ENN) was used to test the effi-cacy of the proposed method. From the study, it was reported that the addition of gamma band improves the accuracy of theclassifier. The best results observed for subjects 1, 2, 3 and 4 were 79.8%, 69.1%, 70.5% and 80.6% respectively.

Liang et al. [4] have implemented a system for classification of mental tasks from EEG signals using extreme learning ma-chine (ELM) and autoregressive features. They inferred that the smoothing of the classifier output improves the recognitionrate of the algorithm. A promising accuracy of 86.70% was achieved for subject 1 in five-class combination using the pro-posed method.

Power spectrum of EEG signals and asymmetry ratio of six different frequency bands (delta, theta, alpha, beta and gam-ma) were used as features [5] and four subjects were utilized. Fisher discriminant analysis and Mahalanobis distance basedclassifiers were employed. Classification experiments were conducted by using pair wise, three-class, four-class and five-class combinations of mental tasks. They demonstrated that the recognition rate for both the classifiers can be further en-hanced by including the higher frequency band (40–100 Hz) information.

Gupta et al. [6] have used only subject 6, power spectral density features and kNN classifier were used. An average accu-racy of 85.71% was obtained for pair wise classification, while 68.68% accuracy was achieved for five-class combinations ofmental tasks. The highest accuracy of 98.63% was achieved for pair wise combination of baseline-rotation tasks.

In [7], researchers have used wavelet packet entropy features and SVM based classifier with only two subjects. The bestaccuracy of 93.0% (subject 1) and 87.5% (subject 2) was obtained by SVM with linear kernel for pair wise classification of men-tal tasks. The classification accuracy for five-class combination of mental tasks was 76.3% for subject 1 and 68.5% for subject 2.

Vijean et al. [8] have used ST based analysis of EEG signals of different mental tasks with only two subjects and performedonly pair wise combination of mental tasks. LDA and kNN classifiers were used as classifiers. An average accuracy of 98.39%for subject 1 and 96.41% for subject 2 was obtained.

From the above discussions, it can be observed that all the aforementioned works share the same database and each con-tribute their own novelty in the field of mental tasks analysis. Direct comparison of their findings is difficult due to the lack ofuniformity in using the number of subjects, performing the types of experiments and presenting the results. Hence, in thiswork, five trials of all the seven subjects from Keirn and Aunon database were used. EEG signals were subjected to featureextraction using ST. The comparison of performance of ST with other feature extraction methods was reported in Table 1.LDA classifier was selected in this work, as it has low computational complexity and immune to over fit [9]. kNN andSVM classifiers were chosen as they were the commonly used algorithm in BCI applications.

3. Methodology

Fig. 1 shows the proposed system for mental task classification. The feature extraction method and classifiers used in thissystem were explained in the following sections.

Please cite this article in press as: Hariharan M et al. Classification of mental tasks using stockwell transform. Comput Electr Eng (2014),http://dx.doi.org/10.1016/j.compeleceng.2014.01.010


Table 1Result comparison with previous works.

Author/year Subjects Feature extraction Pair wise (%) Three class (%) Four class (%) Five class (%)

Rezaei et al. [2] Subject 1, 3, 5, 6 Autoregressive coefficients features S1: 94.07 – – –S3: 89.22S5: 86.58S6: 92.49

Liang et al. [4] Six subjects Autoregressive features – – – S1: 86.70S2: 78.76S3: 64.60S5: 62.79S6: 76.42S7: 79.77

Zhang et al. [5] Four subjects Power spectrum and asymmetryratio of 6 frequency bands

S1: 85.90 S1: 75.30 S1: 66.60 S1: 60.40S2:67.50 S2: 53.80 S2: 45.40 S2: 39.90S3: 72.50 S3: 59.40 S3: 52.10 S3: 46.30S4:84.10 S4: 75.00 S4: 68.60 S4: 64.40

Gupta et al. [6] Subject six Power spectral density S6: 85.71 – – S6: 68.68

Zhiwei and Minfen [7] Two subjects Wavelet packet entropy S1: 93.00 S1: 91.40 S1: 87.06 S1: 76.30S2: 87.50 S2: 80.88 S2: 73.68 S2: 68.50

Vijean et al. [8] Two subjects S-transform based MSR features S1: 98.39 – – –S2: 96.41

Proposed method All seven subjects S-transform based MSR features S1: 98.39 S1: 98.95 S1: 98.54 S1: 97.97S2: 96.41 S2: 93.60 S2: 91.57 S2: 88.78S3: 95.76 S3: 89.92 S3: 87.23 S3: 84.72S4: 98.93 S4: 97.89 S4: 97.24 S4: 97.72S5: 97.83 S5: 97.72 S5: 96.53 S5: 95.12S6: 98.51 S6: 98.37 S6: 97.76 S6: 97.32S7: 97.91 S7: 97.12 S7: 96.14 S7: 94.39

M. Hariharan et al. / Computers and Electrical Engineering xxx (2014) xxx–xxx 3

3.1. Feature extraction using S transform

Time–frequency analysis is extensively used in analyzing EEG signals due to its non-stationary characteristics. Short timeFourier transform (STFT), wavelet transform, and Wigner–Ville transform are the widely used in EEG signal processing,although they have certain limitations. For instance, the window size of STFT is fixed, thereby it cannot provide good timeresolution at high frequency and good frequency resolution at low frequency [10,11]. Although the wavelet analysis can beused to overcome the disadvantages of the STFT, the link between the local frequencies of the STFT is lost [12]. The decom-position of filter bands employed in the transformation also causes the signals to suffer from leakage effect, especially wherethe signal frequency is closer to the edge of a frequency band [13]. This might have major effect on the EEG study whichemphasizes on very small frequency bands for the analysis. There is also a problem of choosing the correct mother waveletfunction for a specific problem as there are many wavelet families. Wigner–Ville transforms gives a high time and frequencyresolution, however, its bilinear structure results in cross-term interferences [12].

Hence, the application of ST in distinguishing EEG signals of different mental tasks was investigated in this work. The STprovides good frequency dependent resolution while maintaining the relationship with the Fourier transform. It combinesthe frequency dependent resolution of the time–frequency phase and absolutely referenced local phase information [12,14].The window of the ST becomes shorter as the frequency increases. Hence, it has a good frequency localization, superior timeand frequency resolution at low frequencies and high time resolution at high frequencies [12]. The combination of STFT fea-tures with the advantages of wavelet function in ST makes it as an ideal feature extraction algorithm to analyze the non-sta-tionary signals like EEG signals. The mathematical explanation of ST algorithm is given as [12]:

The mother wavelet function for a continuous wavelet transform with signal y(t) is given as

Pleasehttp:/

Wðs; dÞ ¼Z 1

�1yðtÞwðt � s;dÞdt ð1Þ

The ST of y(t) signal is defined as phase factor multiplied with specific mother wavelet, thus,

Sðs; f Þ ¼ ei2pfsWðs;dÞ ð2Þ

where the specific mother wavelet is defined as

Wðs; dÞ ¼ fffiffiffiffiffiffiffi2pp e�

t2 f 2

2 e�i2pftdt ð3Þ

cite this article in press as: Hariharan M et al. Classification of mental tasks using stockwell transform. Comput Electr Eng (2014),/dx.doi.org/10.1016/j.compeleceng.2014.01.010


Delta band Gamma bandTheta band Alpha band Beta band

Feature Computations

LDA

Linear Quadratic

10 fold Cross validation

EEG signal (1 frame)

ST matrix 5 features x 6 six channel (active electrodes) = 30 MSR features

k value 1 to 10

Windowing & S Transform

SVM kNN

Mental task classification

Samples

Fig. 1. Block diagram of mental task classification.


It can be noted that the scale parameter d is inverse of frequency, f. Since Eq. (3) does not satisfy the admissibility con-dition for zero mean of a wavelet, Eq. (2) is not strictly a continuous wavelet transform. Therefore, the continuous ST is spec-ified as

Pleasehttp:/

Sðs; f Þ ¼ fffiffiffiffiffiffiffi2pp

Z 1

�1yðtÞe�

ðs�tÞ2 f 2

2 e�i2pftdt ð4Þ

where t and f represents the time and frequency variables.The y(t) signal can be defined in discrete form as y(kT) where T is the sampling interval, the total sampling number N,

k = 0, 1, . . . ,N � 1. The discrete Fourier transform of y(kT) can be obtained as;

Yn

NT

h i¼ 1

N

XN�1

k¼0

yðkTÞe�i2pnkN ð5Þ

where n = 0, 1, . . . ,N � 1.Using Eq. (3), the ST for the time series is obtained by letting s ? jT and f ? n/NT as

S jT;n

NT

h i¼XN�1

k¼0

Ymþ n

NT

h ie�

2p2m2

n2 ei2pmk

N ; n – 0 ð6Þ

For n = 0, the constant is

S½jT� ¼ 1N

XN�1

k¼0

ymNT

h ið7Þ

where j, m = 0, 1, . . . ,N � 1 and n = 1, . . . ,N � 1.




The transformation method yields an m x n ST-matrix whose rows corresponds to frequency and the columns to time. Theelements in the ST-matrix are in complex form. The ST-matrix is obtained as;

Pleasehttp:/

AðkT; f Þ ¼ jS½kT;n=NT�j ð8Þ

3.2. MSR feature computation

ST was used to decompose the signals into five different frequency bands like delta (0.5–4 Hz), theta (4–8 Hz), alpha (8–16 Hz), beta (16–32 Hz) and gamma (32–64 Hz). EEG signals were segmented into frames of 1 s long with 0.8 s overlap. Theneach frame was used to extract the features. Hanning window was used to reduce the leakage effect [15]. The standard devi-ation for each row of the ST-matrix was computed. The mean of square root of the standard deviation (MSR) was used asfeature for the pair wise and multi-class classification of the mental tasks and is given as follows:

MSR ¼ meanffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffistdðSTmatrixÞ

q� �ð9Þ

Five features from a single frame of all the six channels were extracted and formed as a feature vector with 30 dimen-sional features. The extracted features were used as input for LDA, kNN and SVM classifiers.

3.3. Linear discriminant analysis

The LDA classifier reduces the dimensional space while preserving much of the class discriminatory information [16]. Theobjective of LDA is to find a linear transformation that gives maximum class separability. The linear transformation or thediscriminant function is computed as [17,18]:

fiS�1w xt

k � 1=2liS�1w lT

i þ lnðPiÞ ð10Þ

where li is the mean feature in group i (i = 1 and 2), x is the feature of all data, k represents one MSR feature, Pi is the totalsample of each group divided by total samples, SW is the within class scatter matrix. Two discriminate functions (linear andquadratic) were used in LDA classifier.

3.4. k-Nearest neighbor

kNN is a simple, supervised algorithm that employs lazy learning [17,19]. It classifies the test samples based on majorityof k-nearest neighbor’s class. The kNN class is determined by finding the minimum distance between the test samples andeach of the training sets. Each of the query instances (test EEG signal) is compared with each of the training instance (trainingEEG signals). The Euclidean Distance (Eq. (10)) measure is used to find the closest members of the training set to the testclass examined. The label of a class is determined from the kNN category using majority voting.

DEða; bÞ ¼XN

i¼1

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffia2

i � b2i

qð11Þ

where a and b are the training and testing EEG signals composed of N features. The suitable value for k was found as 2through experimental analysis. In Fig. 2, there was a drop in classification accuracy as k increases from 1 to 10 for thefive-class combination of mental tasks.

65

70

75

80

85

90

95

100

1 2 3 4 5 6 7 8 9 10

Acc

urac

y (%

)

k Value

Subject1

Subject2

Subject3

Subject4

Subject5

Subject6

Subject7

Fig. 2. Performance of kNN classifier for five-class combination.




3.5. Support vector machine (one against all)

The SVM classifier operates by finding a separating hyper plane between two classes, such that the minimal distance withrespect to the training vectors is maximized. The non-linear SVM was implemented by applying kernel trick to maximum-margin hyper planes. The feature space was transformed into higher dimensional space where the maximum-margin hyperplane was found [7,8]. SVM theory for 1 against all classifier was explained as follows [4]:

1 Against all SVM was a combinations k number of SVM models. For the ith SVM, the samples in the ith class was trainedwith the positive label (+1) while all the other samples were trained with negative label (�1). Hence, the ith SVM solves theproblem as:

Pleasehttp:/

Minimize12

XN

m¼1

XN

n¼1

ymynKðxm; xnÞaimai

n �XN

m¼1

aim

Subject toXN

m¼1

ymaim ¼ 0 0 6 ai

m 6 C ð12Þ

Solving Eq. (11) and results in k decision function. The maximum of real-valued output among the decision function isused to perform the classification.

Class of x � aug maxi¼1;...;k

XN

m¼1

aimymkðxm; xÞ þ bi

There are several kernel functions commonly used in the implementations of the SVM models,linear, polynomial, sigmoidaland Gaussian kernels. In this work, the Gaussian kernel was utilized as it is commonly used for cognitive mental tasks.

The Gaussian kernel is given by; Kðx; xiÞ ¼ e�ckx�xik2

Feature extraction and classification algorithms were developed under MATLAB 7.0 environment.

4. Results and discussions

The classification results for different combination of the mental tasks (pair wise, three-class, four-class, and five-class)were summarized in Figs. 3–6. Fig. 3 presents the averaged classification accuracy for all the ten pair wise combinations ofmental tasks (BC, BL, BM, BR, CL, CM, CR, LM, LR, and MR). Fig. 4 depicts the averaged classification accuracy for all the tenpossible combinations for three-class mental task classification (BCL, BCM, BCR, BLM, BLR, BMR, CLM, CLR, CMR, and LMR).Fig. 5 provides the averaged classification accuracy for all the five combinations of four-class mental task classification(BCLM, BCLR, BCMR, BLMR, and CLMR). The classification accuracy of five-class mental task classification (BCLMR) wasshown in Fig. 6. The comparison of classification accuracy of the proposed method with the recent works is given in Table1.

From the figures, it can be observed that the mental task classification accuracy for each subject varies respectively. This isdue to the difference in the cognitive process of each person; therefore the mental state of two people performing the sametask might not be the same, which constitutes the differences observed.

By observing the classification results for all the combinations of the mental tasks from Figs. 3–6, it can be observed thatthe kNN classifier yielded maximum average classification accuracy for three-class, four-class and five-class problems com-pared to other two classifiers used. The high recognition rate of the nearest neighbor algorithm suggest that the between-class variability of the MSR feature is high, thus enabling the kNN algorithm to classify the mental tasks accurately based onthe proposed features.

65.00

70.00

75.00

80.00

85.00

90.00

95.00

100.00

Subject 1 Subject 2 Subject 3 Subject 4 Subject 5 Subject 6 Subject 7

Acc

urac

y (%

)

LDA (linear)

LDA (quadratic)

kNN

SVM

Fig. 3. Average accuracy of all pair wise combinations of mental tasks.



65.00

70.00

75.00

80.00

85.00

90.00

95.00

100.00


Acc

urac

y (%

)

LDA (linear)

LDA (quadratic)

kNN

SVM

Fig. 4. Average accuracy of all three-class combinations of mental tasks.

65.00

70.00

75.00

80.00

85.00

90.00

95.00

100.00


Acc

urac

y (%

)

LDA (linear)

LDA (quadratic)

kNN

SVM

Fig. 5. Average accuracy of all four-class combinations of mental tasks.

65

70

75

80

85

90

95

100


Acc

urac

y (%

)

LDA (linear)

LDA (quadratic)

kNN

SVM

Fig. 6. Average accuracy of five-class combinations of mental tasks.


LDA (quadratic) and SVM classifier performed comparatively better during pair wise classification of mental tasks com-pared to kNN classifier, but for the multi-class problems its performance was decreased. This might be due to the increase incomplexity of the algorithm, as SVM was originally developed to classify binary problems. Therefore, this increase in com-plexity of the problem affects the classification rate of the SVM classifier. Although the LDA classifier performs well in pairwise classification of the mental tasks, its prediction rate drops as the number of classes increases. This suggests that thelinear algorithm is not able to handle complex problem, and is only suitable for handling binary classifications.

From Table 1, it can be observed that Rezaei et al. have used the autoregressive coefficients to perform pair wise classi-fication of the mental tasks for subjects 1, 3, 5 and 6. The best average accuracy achieved for subject 1 was 94.07% while theproposed method was able to produce 98.39% accuracy for the same subject. The proposed method also outperforms theclassification results of subject 3, 5 and 6 in their study.

Although Gupta et al. [6] have employed power spectral density features with kNN classifier using EEG signals of subject6, they have attained a classification accuracy of 85.71% only for pair wise classification and 68.68% for five class classifica-tion using Kullback Leible (KL) distance. Comparatively, the proposed method was able to produce 96.10% during pair wiseclassification and 97.32% during five-class combination of mental tasks for the subject 6 using the same classifier.

From Table 1, it can also be inferred that Zhiwei and Minfen have used wavelet packet transform to investigate the time–frequency characteristics of the five different mental tasks for subject 1 and subject 2. Using SVM classifier, they were able to




achieve classification accuracy between 68.50% and 93.00% in distinguishing the different combinations of mental tasks. Inour work, we have obtained classification accuracy from 88.78% to 98.95% for the classification of the different combinationsof mental task using our proposed ST based analysis of EEG dynamics with the three supervised classifiers (subjects 1 and 2).

The similar trend can also be observed in all classification experiments as reported in Table 1, where the proposed methodwas able to outperform the studies in the literature. The experimental results show that ST based time–frequency analysisreveals hidden information for better classification of mental tasks using EEG dynamics.

5. Conclusion

Generally, BCI systems can be categorized into systems operating in cue-paced (synchronous) and self-paced (asynchro-nous). In synchronous BCI, the users are asked to generate commands only during specific periods. Researchers have pro-posed different feature extraction and classification methods for synchronous BCI application. In this paper, we have alsoproposed a feature extraction method based on ST for synchronous BCI application. EEG signals of different mental taskswere decomposed using ST. From the ST matrix, MSR features were extracted and tested using three different classifiersnamely LDA, kNN and SVM. On the whole, the average accuracy obtained for all the 7 subjects from Keirn and Aunon data-base ranges from 66.02% to 98.95% and it indicates the strength of the proposed method. Among the three supervised clas-sifiers, kNN classifier performed better in all the classification experiments than LDA and SVM classifiers. In this work, wehave conducted subject dependent classification experiments. The improvement in the mental tasks classification by theproposed method can provide a better communication pathway between the brain and the machines, which in turn createsa pathway for development of more reliable assistive devices such as brain wave controlled wheel chairs, prostatic limbs andsmart living environment for physically disabled. In the future, the proposed method will be extended to perform subjectindependent mental tasks classification. Different types of features will be derived from ST matrix and their usefulness willalso be investigated. The proposed method will also be applied and tested in an asynchronous BCI application, as the onlinetraining and adaptation is a great challenge in this application.

Acknowledgments

This work is supported by Fundamental Research Grant Scheme (FRGS), Malaysia. Grant Number: 9003-00297. Theauthors thankfully acknowledge Dr. C.W. Anderson, Colorado State University for providing the EEG data.

References

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M. Hariharan is currently working as a Senior Lecturer in Universiti Malaysia Perlis. He has obtained Bachelor of Engineering in Electrical and Electronicsfrom Bharathiar University, India and a Master degree from Anna University, India. He has completed Ph.D in Engineering at Universiti Malaysia Perlis(UniMAP), Malaysia. His research interests include speech signal processing, wavelet transform, image processing and artificial intelligence. He is a SeniorMember of IEEE, USA and a Member of IET, UK.


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Vikneswaran Vijean received his Bachelor of Engineering in Mechatronic from Universiti Malaysia Perlis in 2010 and he is currently a postgraduate studentat UniMAP. His research interests include signal and image processing, artificial intelligence, evoked potentials, and BCI applications. He is a studentmember of IEEE, USA.

R. Sindhu is a Postgraduate student in Universiti Malaysia Perlis, Malaysia. She has completed her Bachelor of Technology in Information Technology fromAnna University in 2009. Her research interest includes machine learning algorithms and hybrid optimization algorithms.

P. Divakar received his Bachelor of Engineering in Mechatronics from Anna University, India and Master of Science in Mechatronic Engineering fromUniMAP. He is currently working as an application specialist in biomedical field. His research interests include signal and image processing, artificialintelligence and brain computer interface.

A. Saidatul is a lecturer at Universiti Malaysia Perlis, Malaysia. Her research interests include signal processing, brain mapping, artificial intelligence and BCIapplications.

Sazali Yaacob is currently working as a Professor at UniMAP, Malaysia. He has published more than 150 papers in referred Journals and ConferenceProceedings. His research interests are in Artificial Intelligence applications in the fields of acoustics, vision and robotics. He received Charted Engineerstatus by the Engineering Council, United Kingdom in 2005 and also a Member of IET, UK.



classification of mental tasks using stockwell transform

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