ORIGINAL RESEARCH ARTICLEpublished: 02 September 2013

doi: 10.3389/fnhum.2013.00516

Temporal hemodynamic classification of two handstapping using functional near—infrared spectroscopyNguyen Thanh Hai1, Ngo Q. Cuong2, Truong Q. Dang Khoa1* and Vo Van Toi1

1 Biomedical Engineering Department, International University of Vietnam National Universities in Ho Chi Minh City, Ho Chi Minh City, Vietnam2 Department of Electronics and Telecommunications, Faculty of Electrical and Electronics Engineering, University of Technical Education HCMC, Ho Chi Minh City,

Vietnam

Edited by:

Nobuo Masataka, Kyoto University,Japan

Reviewed by:

Carlo Cattani, University of Salerno,ItalyThang M. Hoang, University ofCanberra, Australia

*Correspondence:

Truong Q. Dang Khoa, BiomedicalEngineering Department,International University of VietnamNational Universities in Ho Chi MinhCity, Quarter 6, Linh Trung Ward,Thu Duc District, Ho Chi Minh City,Vietname-mail: khoa@ieee.org

In recent decades, a lot of achievements have been obtained in imaging and cognitiveneuroscience of human brain. Brain’s activities can be shown by a number of differentkinds of non-invasive technologies, such as: Near-Infrared Spectroscopy (NIRS), MagneticResonance Imaging (MRI), and ElectroEncephaloGraphy (EEG; Wolpaw et al., 2002;Weiskopf et al., 2004; Blankertz et al., 2006). NIRS has become the convenient technologyfor experimental brain purposes. The change of oxygenation changes (oxy-Hb) along taskperiod depending on location of channel on the cortex has been studied: sustainedactivation in the motor cortex, transient activation during the initial segments in thesomatosensory cortex, and accumulating activation in the frontal lobe (Gentili et al., 2010).Oxy-Hb concentration at the aforementioned sites in the brain can also be used as apredictive factor allows prediction of subject’s investigation behavior with a considerabledegree of precision (Shimokawa et al., 2009). In this paper, a study of recognition algorithmwill be described for recognition whether one taps the left hand (LH) or the right hand (RH).Data with noises and artifacts collected from a multi-channel system will be pre-processedusing a Savitzky–Golay filter for getting more smoothly data. Characteristics of the filteredsignals during LH and RH tapping process will be extracted using a polynomial regression(PR) algorithm. Coefficients of the polynomial, which correspond to Oxygen-Hemoglobin(Oxy-Hb) concentration, will be applied for the recognition models of hand tapping.Support Vector Machines (SVM) will be applied to validate the obtained coefficient datafor hand tapping recognition. In addition, for the objective of comparison, Artificial NeuralNetworks (ANNs) was also applied to recognize hand tapping side with the same principle.Experimental results have been done many trials on three subjects to illustrate theeffectiveness of the proposed method.

Keywords: polynomial regression algorithm, support vector machines, artificial neural networks, hand tapping

recognition, functional Near-Infrared Spectroscopy

INTRODUCTIONHuman brain has a complex structure with around 100 billionneurons, so it is a big challenge for all scientists in biological com-puting (Wolpaw et al., 2002). These neurons can communicatefrom one to another with or without external excitations to maketypical decisions (pattern recognition, cognition, motion, andothers; Critchley, 2009). Moreover, in prefrontal cortex of humanbrain plays an important role in social activity for both adultsand children. Tobias Grossmann represented a review related tothe role of prefrontal cortex of human brain, in which specificareas in the adult human brain as social brain could process thesocial world (Aydore et al., 2010; Grossmann, 2013) and also TilaTabea Brink et al. investigated about orbitofrontal cortex in chil-dren with 4− to 8-year-old through processing empathy stories(Brink et al., 2011). The result is that children could passivelyfollow these stories presenting social situations. Regarding pre-frontal cortex, EEG electrodes were mounted on frontal positionsof human brain for wheelchair control (Ahmed, 2011). In par-ticular, user could move eyes to drive the electrical wheelchair toreach the desired target.

In recent decades, a lot of achievements have been obtainedin imaging and cognitive neuroscience of human brain. Brain’sactivities can be explored using different kinds of non-invasivetechnologies, such as: Magnetic Resonance Imaging (MRI), Near-Infrared Spectroscopy (NIRS), and ElectroEncephaloGraphy(EEG; Wolpaw et al., 2002; Weiskopf et al., 2004; Blankertzet al., 2006; Ince et al., 2009). Many researchers have beenattracted by these technologies with many approaches to findout problems related to human brain for rehabilitation andtreatment. For the rehabilitation problem, information obtainedfrom human brain using EEG technique could be employed toperform shared control of motion wheelchairs (Tanaka et al.,2005). A brain simulator can lead to improve or to recover thecognitive/motor functions of tetraplegic patients with degen-erative nerve diseases spinal cord injuries (Kauhanen et al.,2006). In these non-invasive technologies, the NIRS technol-ogy is often applied to measure Oxygen Hemoglobin (Oxy-Hb), deOxy-Hb, and Total-Hb concentration changes. Thesechanges allow us predict brain activations related to bodybehaviors.

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HUMAN NEUROSCIENCE

Thanh Hai et al. Temporal hemodynamic classification using NIRS

fNIRS has become the convenient technology for experimentalbrain purposes. This non-invasive technique emits near infraredlight into the brain to measure cerebral hemodynamics as wellas to detect localized blood volume and oxygenation changes(Tsunashima and Yanagisawa, 2009). The change of oxy-Hb alongtask period depending on the location of channels the cortex hasbeen studied: sustained activation in the motor cortex, transientactivation during the initial segments in the somatosensory cor-tex, and accumulating activation in the frontal lobe (Gentili et al.,2010). Oxy-Hb concentration at the aforementioned sites in thebrain can also be used as a predictive factor allows predictionof subjects’ investigation behavior with a considerable degree ofprecision (Shimokawa et al., 2009).

fNIRS technique is a non-invasive technique which is appliedto monitor human body for diagnosis and treatment (Bozkurtet al., 2005; Macnab et al., 2011; Reher et al., 2011). HiroshiTaniguchi et al. investigated six subjects with unilateral spa-tial neglect (USN)-positive (+) and 6 others with USN-negative(Taniguchi et al., 2012). In this research, brain activity was sim-ulated by prism adaptation tasks using fNIRS. The result showedthat there was a typically great reduction in Oxy-Hb of the USN(+). For monitoring carotid endarterectomy, one was appliedthe NIRS technique to evaluate its reliability in the detectionof clamping ischemia (Pedrini et al., 2012). The result foundthat there were three patients who represented transient ischemicdeficits at awakening and no case of perioperative stroke or death.

In addition, fNIRS technique has been appeared as an alterna-tive brain-based experimental technique (Lloyd-Fox et al., 2010)to measure human thoughts and activities for rehabilitation. Forevaluating behaviors related to human brain during experiments,subjects feel free for performing his or her brain activities. In par-ticular, this technique has been successfully used to study brainfunctions such as assessment of motor task from everyday liv-ing, athletic performance, recovery from neurological illness (Huet al., 2010), assessment of verbal fluency (Schecklmann et al.,2010), and quantification of brain function during finger tap-ping (Sato et al., 2007). However, to the best of our knowledge,there have been a few applications of the fNIRS technique toquantify the motor control signals leading to brain simulator forrehabilitation (Chunguang et al., 2010; Gentili et al., 2010).

Neural networks can be used for cognition brain tasks as a clas-sification module, in which wavelet decomposition can be used asfeature extractions (Khoa and Nakagawa, 2008); wavelet can beused to remove artifacts (Molavi and Dumont, 2010). Base on theslope of straight line, hand side tapping can be distinguished (Ngoet al., 2012). Oxy-Hb and Deoxy-Hb can also be used directlywith SVM algorithm for the recognition of hand tapping (Sitaramet al., 2007).

Savitzky–Golay (SG) filters have been used to smooth signalsand images with noises as well as artifacts in recent years. In theSG filters, the coefficients of the local least-square polynomial fitare pre-computed to preserve higher movements and then theoutput of the filter is taken at the center of the window (Savitzky,1964; Steinier et al., 1972; Gorry, 1990). In this paper, the SGfilter was applied to reduce spike noises of Oxy-Hb signals. TheOxy-Hb signals after filtering allow us be easier in recognizing left(LH) or right hand (RH) tapping status. Moreover, a Polynomial

Regression (PR) approach has been applied for estimation of sig-nals and images with noise (Cui and Alwan, 2005; Cai et al., 2007;Zhang et al., 2009; Khan et al., 2011). In our research, in order toestimate Oxy-Hb signals, the PR algorithm was used to producepolynomial curves with their features. Based on these features,one can classify tapping hand tasks.

Support Vector Machine (SVM) algorithms have been appliedfor classification problems in the machine learning communityin recent years. In this case, the SVM was employed to clas-sify hypothyroid disease based on UCI machine learning dataset(Chamasemani and Singh, 2011). Another application related tomedical images is that the SVM was utilized to recognize the leafspectral reflectance with different damaged degrees in the imageprocessing and spectral analysis technology (Dake and Chengwei,2006). In this project, the SVM algorithm (Sitaram et al., 2007)was applied to recognize hand tapping tasks using fNIRS technol-ogy. Oxy-Hb signals after reducing noise will be extracted featuresusing a PR algorithm. Based on coefficients obtained from the PR,the SVM algorithm will be applied for the recognition of the LHand RH tapping tasks.

Another algorithm for classification is that a recursive train-ing algorithm for EEG signals using Artificial Neural Networks(ANNs) to generate recognition patterns from EEG signals wasproposed to control electric wheelchair (Tanaka et al., 2005;Singla et al., 2011). Mental tasks were classified for wheelchaircontrol using prefrontal EEG (Rifai Chai, 2012). The relevantmental tasks used in this paper are mental arithmetic, ringtoneimagery, finger tapping, and words composition with additionaltasks which are baseline and eyes closed. The feature extractionis based on the Hilbert Huang Transform (HHT) energy methodand then the ANNs with the Genetic Algorithm (GA) optimiza-tion (Subasi et al., 2005) was applied for classification. The resultis that the accuracy of the proposed classification algorithm withthe five subjects participated was between about 76 and 85%.

In this paper, we proposed the recognition algorithm fordeveloping a brain computer interface using fNIRS. First of all,Savitzky–Golay filter is used to reduce noises as well as artifacts.Coefficients, which are features of Oxy-Hb signals, are foundby using a PR algorithm. For the recognition of tapping handsrelated to the left and right brain activation, ANN and SVMalgorithms were used. These two methods will be compared tofind out the best one. The results and discussion about tappinghand activity will be shown to illustrate the effectiveness of theproposed approaches. This process is shown in Figure 1.

MATERIALS AND METHODSSUBJECTS AND THE EXPERIMENTAL SETUPA multichannel fNIRS instrument, FOIRE-3000 (SHIMAZU Co.LTD, Japan), is used to acquire brain Oxy-Hb. This machinewas located at Lab-104 of Biomedical Engineering Department,International University, VNU, Vietnam. The FOIRE 3000 systemwith the eight pairs of the probes, consisting of the illumina-tor and detector optodes, produces 24 channels as shown inFigure 2A. These probes were placed on the scalp to collect fNIRSdata, in which the detectors were installed at a 3 cm distance fromthe illuminators. The optodes were arranged to install at the lefthemisphere on the head of the subject.

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Oxy-Hb concentration changes in motor control area ofhuman brain was captured from a set of the holder with 24 chan-nels for both hemispheres using the fNIRS technique as shown inFigure 2B. In particular, when the subject performs one typicalactivity, brain signals will be obtained from the fNIRS systemand then calculated to produce three types of signals [Oxy-Hb(red), Total-Hb (green) and Deoxy-Hb (blue)] corresponding tothree wavelengths (780, 805, and 830 nm), in which [Total-Hb] =[Oxy-Hb] + [Deoxy-Hb]. Moreover, the distance between pairsof emitter and detector probes was set at 3 cm and all probes wereattached with holders arranged on different sides of human brainhemispheres depending on users. Concentration changes of threesignals produce time points in an output. In this research, Oxy-Hb changes are calculated in the following formula (ShimadzuCorporation, 2010):

Oxy = −3.6132 ∗ Abs[780 nm] + 1.1397 ∗ Abs[805 nm]+ 3.0153 ∗ Abs[830 nm] (1)

in which Abs: Absorbance.Three subjects (male, average: 25 years old, 60 kg weights,

right-handed) were participated into this study. All participants

FIGURE 1 | Recognition algorithm block diagram. First of all,Savitzky–Golay filter is used to reduce noises as well as artifacts. After that,feature of Oxy-Hb is found by a polynomial regression based on itscoefficients. Finally, Artificial Neural Network or Support Vector Machines isused to determine whether left hand or right hand is tapped.

were healthy and showed no musculoskeletal or neurologicalrestrictions or diseases. Before participating into the experiments,each subject was asked to fill out a questionnaire consisting ofpatient’s identification, age and gender, which was kept confi-dential. The tenets of the Declaration of Helsinki were followed;the local Institutional Review Board approved the study. Thesesubjects informed consent agreement after reading and under-standing of the experiment protocol and the fNIRS technique.

After reading and understanding the experiment protocol andthe fNIRS technique, he will start doing hand tapping. The sub-ject was required to perform hand tapping motions, both left andright sides as motor activities. In these hand tapping motions, aprotocol includes 20 s (Rest)—20 s (Task)—20 s (Rest), it meansthat the subject relaxed in 20 s, tapped his hand up/down about10 times in 20 s, and then rested 20 s, as shown in Figure 3.

Oxy-Hb data were collected on 20 channels, in which 10 chan-nels are of the left brain side and that of the opposite side will beobtained for hand tapping recognition. However, we just chose 4channels of each side which focus on hand and leg motion areato analyze and to estimate features. In particular, the left brainchannels are 2, 5, 6, 9, and the 12, 15, 16, 19 channels are of theright brain side as in Figures 4A,B. In this research, Oxy-Hb dataobtained from these channels will be processed to recognize handtapping tasks. Without loss of generality, the natural architectureis different from person to person. The probes are allocated on theholder, in which the transmitter probes and receiver probes arepredicted to cover as much as area of brain based on the physicalstructure of each subject. The authors (Aihara et al., 2012) com-bined EEG and NIRS for estimation of cortical current source.The probes position using stylus marker to allow co-registration

FIGURE 2 | (A) fNIRS FOIRE-3000 system. This system operates at threedifferent wavelengths of 780, 805, and 830 nm. (B) Subject’ head withinstalled probes. The distance between pairs of emitter and detectorprobes was set at 3 cm and all probes were attached with holders.

FIGURE 3 | Setting of experiment protocol. The subject relaxed in 20 s,tapped his hand up/down about 10 times in 20 s, and then rested 20 s.

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FIGURE 4 | Probes location and channels on two hemispheres. (A) Probes location (red—emitter, blue—detector) and channels on the motor control areaof the left hemisphere. (B) Probes location and channels (yellow) on the motor control area of the right hemisphere.

of EEG and NIRS results. In this paper, we also used marker tofind out the average positions of motor area of human brain cor-tex. To achieve more accuracy, the NIRS activity was mapped ontocerebral cortex using fusion software (Shimadzu Corporation,2010). From this evidence, we proposed the selection of channels2, 5, 6, 9 and 12, 15, 16, 19 for hand tapping recognition with the20-channel NIRS system configured above.

DATA PRE-PROCESSINGBrain data of a subject acquired from the channels have noise andartifacts. In order to obtain more smoothly brain data, a filter asthe Savitzky–Golay filter was applied in this paper. The Savitzky–Golay filters (Orfanidis, 2010) are also known as polynomialsmoothing. It means that the idea of the polynomial smoothing isreplacing samples of signal by the values that lie on the smoothingcurve. In moving an average FIR filter, the output is a simply aver-age version of its inputs, in which this filter has the response ofthe low-pass filter. In practice, NIRS signals fluctuate along timecorresponding to excitations and have the unknown specific fre-quency. Therefore, it could not be the average of inputs with thearbitrary FIR filter length. In this research, to track the acquiredsignal, the Savitzky–Golay filter as the FIR filter can be used.

In general, we can evaluate a polynomial with the order of dto smooth the length-N data x with the condition N ≥ d + 1.Assume that, the data x is the type of a vector

x = [x−M, . . . , x−1, x0, x1, . . . , xM]T (2)

in which N samples of x are replaced by the polynomial with theorder of d as follow:

xm = c0 + c1m + · · · + cdmd, −M ≤ m ≤ M (3)

where c0, c1, . . ., cd denote polynomial coefficients. M is thenumber of points on either side of x0

In this case, there are d + 1 based on the vector si, i = 0, 1, . . .,d as follows:

si(m) = mi, −M ≤ m ≤ M (4)

Thus, we can write the vector S as follows:

S = [s0, s1, . . . , sd] (5)

in which s0, s1, . . ., sd are the polynomial basic vectors.The smooth values in (3) can be re-written in the following

equation:

x =d∑

i = 0

cisi = [s0, s1, . . . , sd]

⎡⎢⎢⎢⎢⎣

c0

c1...

cd

⎤⎥⎥⎥⎥⎦ = Sc (6)

Coefficients of the desired filters are obtained as follows:

B = SGT = GST = SF−1ST = [b−M, . . . , b0, . . . bM] (7)

in which, b−M , . . ., b0, . . ., bM are the column filters of theSavitzky–Golay filter set.

{F = ST S

G = SF−1 (8)

Finally, the values to create more smoothly signals are estimatedin the following equation:

xm = bTmx, m = −M, . . . , 0 . . . , M (9)

in which, bTm are the transpose version of bm.

In this paper, the Savitzky–Golay filter will be utilized tosmooth spikes of brain Oxy-Hb signals for identifying hand tap-ping tasks. The filtered Oxy-Hb signals allow us extract featureswith reliable information.

FEATURE EXTRACTIONIn general, the first step in classification work is to find thefeatures of data samples. For this purpose, there are many meth-ods such as Principle Component Analysis (PCA), IndependentComponent Analysis (ICA) and etc. However, hemodynamic

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Thanh Hai et al. Temporal hemodynamic classification using NIRS

response of human brain changes in time domain. Moreover,we want to evaluate the Oxy-Hb concentration corresponding tohand tapping tasks based on analyzing numeric as well as havinga look in graphical figures.

PR algorithm (Montgomery and Runger, 2003) presents therelationship between amplitude and time of a signal. In this paper,the PR algorithm was applied to analyze brain Oxy-Hb data inblood flow corresponding to hand tapping tasks. From the pro-cessed data, one can distinguish the difference between the LHand RH tapping times corresponding to the difference of theOxy-Hb concentration changes.

Assumed that we have the set of two-dimensional data,(x1, y1), . . ., (xn, yn), where each of x and y has no informationabout the other. Our problem is fitting a polynomial curve gener-ated by a typical data. Thus, the relationship between x and y canbe found out. Based on the coefficients of the regression curvewith the order of 5, one can estimate the hand tapping. In partic-ular, the PR equation between independent variable x and y fittedcan be expressed as:

y = h0 + h1x + h2x2 + · · · + hmxm (10)

in which, h0, h1, h2, · · · , hm are estimated values of h0,h1, h2, · · · , hm. There are m regressors and n observa-tions,

(xi1, xi2, · · · , xim, yi

), i = 1, 2, . . ., n corresponding to(

xi, x2i , · · · , xm

i , yi). In this equation, the powers of x play the role

of different independent variables.The PR model can be re-written as a system of linear equations

y = Xh + ε (11)

where: ε = [ε1, ε2, · · · , εn]T is a vector of error.The ordinary least square h of h given by the arguments that

minimize the residual sum of squares and the distributive law isemployed. One obtains the equation,

RSS(h) = y′y + h′(X′X)h − 2y′Xh (12)

Equation 12 is minimized by taking ∂RSS∂h and set the result to zero.

This leads to

X′Xh = X′y (13)

The ordinary least square in the case of the inverse of X′X exists isgiven by

h = (X′X

)−1X′y (14)

From these coefficients, one can determine problems of the LHtapping or RH tapping tasks with the measured brain data usingthe fNIRS technology. Figure 5 represents the regressed signalof the channel-2 corresponding to Equation 15. Similarly, theregression signals of channels 5, 6, 9, 12, 15, 16, and 19 can beshown.

yC2 = −0.0001x5 + 0.0023x4 − 0.0114x3 + 0.0182x2

+ 0.0043x − 0.0329 (15)

FIGURE 5 | The regression signal of filtered channel 2. Sudden changeshad been removed with the window size of 11.

In this figure, the blue Oxy-Hb signal after the Savitzky–Golayfilter was calculated to produce the red regressive curve. Eachhand tapping creates the regressive Oxy-Hb curves which containinformation or its feature coefficients. For recognition of handtapping types, these coefficients will be given the input of theidentification system or called the identification algorithms.

The regressed polynomial must represent the original signalwith the best fit. The smaller error between the origin (here isthe filtered NIRS signal) and the regressed signal is higher thanthe order of the polynomial is. It means that one should choosethe order not only to fit the origin but also to show the gen-eral trend and the characteristic of NIRS signal. In practice, theNIRS signal can not change immediately at the moment of tap-ping hand. For example, one hand moving up or down will makean excitation to both hemispheres. Therefore, in 20 s of tappinghand, one person could take 10 times of moving hand up anddown. In this case, Oxy-Hb level, which will flow from the low-est to highest level in short time, is not the “trend” of overallsignal. This is the reason for choosing the polynomial with theorder of 5.

ARTIFICIAL NEURAL NETWORKANNs are the very powerful tools for the problems of classifi-cation and pattern recognition. We can use the estimated coef-ficients as the features from the PR algorithm by connecting witha multilayer feed forward network for recognition. The archi-tecture of this network used here consists of an input layer, onehidden layer, and the output layer as shown in Figure 6. In par-ticular, input samples are the features from channel coefficientscorresponding to Oxy-Hb concentration changes. The number ofhidden nodes is carefully chosen for this case to obtain higher per-formance. Therefore, it can be chosen as an average of number ofthe input nodes and the output nodes. With the hidden layer, weused the double sigmoid function and this sigmoid function wasalso used for the output layer.

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FIGURE 6 | Architecture of classification network. This net has 48 nodesinput, 100 nodes at hidden layer and 2 nodes at output.

In general, standard back propagation is used for training thenetwork with three layers. It is a gradient descent algorithm,in which the network weights are moved along the negative ofthe gradient of the performance function. With this argument,the training is based on the minimization of the following errorfunction:

E =N∑

n = 1

(on − dn)2, (16)

where N is number of samples, o is network output and d isdesired output.

Suppose that the network has I nodes of the input layer, Jnodes of the hidden layer and the output layer is K nodes. Call

w(1,0)j,i is weight from the ith node of the input layer to the jth node

of the hidden layer and w(2,1)k,j is weight from the jth node of the

hidden layer to the kth node of the output layer. The backpropa-gation learning of the 3-layers network is shown in Table 1. Theapplication is that with the LH tapping, the output is desired toget the value of [1; 0] and [0; 1] is the desirable value of the righttapping. The ANN is one of the approaches which is often used forrecognition. In this research, the SVM is also applied to identifyhand tapping tasks through Oxy-Hb flowing in brain blood.

SUPPORT VECTOR MACHINESIn order to estimate hand tapping tasks, after determining coef-ficients of hand tapping times using the PR algorithm, we alsoused the linear SVM algorithm (Shawe-Taylor, 2000) to vali-date the coefficient data. In the linear SVM algorithm, assumethat the training data are {xi, yi}, i = 1, . . .,l ,yi ∈ {−1, 1},xi ∈ Rd.The points x which lie on the hyperplane satisfy w.x + b = 0, inwhich | b |/‖ w ‖ is the distance from the hyperplane to the ori-gin (where ‖ w ‖ is the Euclidean norm of w). Let d+ (d−) bethe shortest distance from the seperation hyperplane to the clos-est positive (negative) samples corresponding to the coefficientsof LH tapping and RH tapping, respectively. This is showed inFigure 7.

Table 1 | The three-layers network with backpropagation learning.

Random initial weightsWhile the Mean Square Error (MSE) is unsatisfied or the number ofepochs is not exceed,

For each input xp, 1 ≤ p ≤ P, (*)

Compute the inputs of hidden layer net(1)

p, j ;

Compute the outputs of hidden layer x(1)

p, j ;

Compute the inputs of ouput layer net(2)

p, k ;

Compute the outputs of network op, k ;

Modify outer weights

�w (2, 1)

k, j = η(dp, k − op, k

)S′

(net(2)

p, k

)x(1)

p, j

Modify weights between input layer and hidden layer

�w (1, 0)

j, i = η

k∑k = 1

((dp, k − op, k

)S′(net(2)

p, k

)w (2, 1)

k, j

)S′(net(1)

p, j

)xp, i

End (*)

End While

Where: S() is the active function, η is the learning rate.

FIGURE 7 | Linear seperation hyperplane for right hand tapping feature

and left hand tapping.

Margin of the hyperplane is d+ + d−. In the linear case,the support vector looks for the separating hyperplane with thelargest margin using the primal Lagrangian. Suppose that alltraining data satisfy the following contraints:

xi · w + b ≥ +1, for yi = +1 (17)

xi · w + b ≤ −1, for yi = −1 (18)

The optimization problem is considered to transform Equations17 and 18 using the primal Lagrangian as follows:

Lp(w, b, α) = 1

2||w||2 −

l∑i = 1

αiyi(xi · w + b) +l∑

i = 1

αi (19)

where αi ≥ 0 are the Lagrange multipliers.Differentiating Lp with respect to w and b and then getting the

results to zeros, we have the following equation:

∂Lp(w, b,α)

∂w= w −

l∑i = 1

yiαixi = 0 (20a)

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FIGURE 8 | Raw data and its smoothed version. (A) Raw and smoothed NIRS data of channel 2, 5, 6, 9 of the left hemisphere. (B) Raw and smoothed NIRSdata of channel 12, 15, 16, 19 of the right hemisphere.

∂Lp(w, b,α)

∂b=

l∑i = 1

yiαi = 0 (20b)

Equations can be re-written to calculate the support vector asfollows:

w =l∑

i = 1

yiαixi (21)

The regressed data will trained using the SVM method, in whichthe hyperplane is a linear function and divided into two planes:

D+ contains the coefficients and y = +1 is of the left tapping;similarly D− has the coefficients and y = −1 is of the righttapping.

RESULTS AND DISCUSSIONOxy-Hb raw signals (blue) were collected from the fNIRS systemusing the proposed protocol (see Figure 5) which plays an impor-tant role during measure tasks. In particular, each subject tappedhis hand up or down 10 times in 20 s. Therefore, we could sep-arate this task into 10 parts, in which each part has 1 s up and1 s down as shown in Figure 9. Before analyzing Oxy-Hb signals,

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FIGURE 9 | Smoothed signal analysis. Black dash line shows tappingtime period while the green solid curve shows the changes in theory forchannel 2.

the Savitzky–Golay (SG) filter was chosen to produce the smoothOxy-Hb signals (red) as shown in the Figures 8A,B. In this filter,if the size of the window is too small, noises still affect upon theOxy-Hb signals. Otherwise, if the large window size is chosen, theuseful information may be lost. As mentioned before, Oxy-Hbsignals using fNIRS technique are the concentration of Oxy-Hbin blood flow of human brain related to excitations or activitiesof human body. Therefore, choosing the window size as well asthe order of the filter is very important and also depends on eachtypical case. For this reason, the SG with the window size of 11and the order of 3 was applied (see Figures 8A,B).

After smoothing Oxy-Hb signal by using the SG filter, thefeatures of Oxy-Hb signal corresponding to hand tapping areextracted using a PR algorithm. In this case, the PR algorithmwith the order-5 polynomial produces six coefficients and itsequation is represented as follows:

y = h5x5 + h4x4 + h3x3 + h2x2 + h1x + h0 (22)

where x represents time from 0 to 7 s with the resolution of 0.07.The fact is that choosing the window size as well as the order

of the polynomial plays an important role due to avoidance ofloosing information of signals. In Figures 8, 9, the red Oxy-Hbsignals are the smoothed signals, in which the window size andthe order were carefully calculated and chosen so that the peaksof the signals removed after filtering do not affect consequenceson the analysis.

This equation was applied to determine the regressed Oxy-Hbsignals of the channels 2, 5, 6, 9 (left hemisphere) and the chan-nels of the right hemisphere, 12, 15, 16, 19. Thus, the obtainedresults of the RH tapping and the LH tapping as showed inFigures 10A,B are compared together. However, these features ofthe Oxy-Hb signals obtained at two hemispheres are very hard todistinguish between are the right tapping and the left tapping. For

this reason, training data, which are coefficients of the regressedpolynomials as shown in Table 1, were applied to identify handtapping tasks. In particular, in each time of hand tapping, Oxy-Hb concentration changes of two hemispheres allow us obtain theregressed coefficients using the PR algorithm. Moreover, six coef-ficients of each channel as shown in Table 2 are arranged to bea vector. For classification of hand tapping tasks, the vector wasemployed to the algorithms such as the ANN or SVM for trainingdata.

Assume that vr is the vector of the RH tapping and the vectorof the LH tapping is vl. In one run of experiment, the subject per-formed a hand tapping task 20 times, in which 10 times for the LHtapping and 10 times for the RH one. Therefore, a set of the LHtapping coefficients Sl includes 10 vectors (from vl1 to vl10) andthat of the RH tapping coefficients Sr is 10 vectors (from vr1 tovr10). With 80 sample vectors obtained from subjects, the recog-nition algorithm was worked out by splitting the sample vectorsto be 4 runs of 20-fold cross recognition. For identifying the LHtapping, one used 9 vectors of the Sl set combined with the 10vectors of the Sr set and the remaining vector of the Sl set is usedto be a sample vector for identification. In the case of identify-ing the RH tapping, 9 vectors of the Sr set combined with the 10vectors of the Sl and the remaining one is used to be the samplevector for identifying. As known, Oxy-Hb signals obtained fromhuman brain have many noises and artifacts. Therefore, identify-ing hand tapping tasks corresponding to Oxy-Hb concentrationchanges is not easy. For this reason, the identification algorithmssuch the ANN and SVM are reliable in this research. In the SVMmethod, the linear hyperplane was chosen. In each training pro-cess, the values α (having 15 values of α) are produced, also thereare 15 support vectors w (each vector w is 48 elements) and b is0.068. In similarity, the ANN algorithm with the hidden layer of100 nodes was applied to obtain the training result, in which thegoal of training is set up of 0.001 and the number of epochs is5000.

From the data sets of the hand tapping tasks, the SVM algo-rithm was applied for learning to analyze data and recognizepatterns. In understanding this SVM training algorithm, datavectors from the hand tapping tasks are given the input of the clas-sifier with a hyperplane which forms two possible classes of theoutput. In this method, experimental results to the LH tappingof Subject-1 and Subject-3 are the same and Subject-2 showedthe lower performance with just 72.5% of the accuracy com-pared with 82.5% of Subject-1 and Subject-3 as shown in Table 3.While basically its results are the same to that of tapping theRH side.

The ANN algorithm used for identifying hand tapping taskshere consists of one input layer, one hidden layer of 100 nodesand the output layer with two nodes. In addition, the secondmethod in this paper is one of recognition methods which havebeen applied in recent years. Although this algorithm is usedvery popular for recognition problems, it still uses here due togiving the good performances and also being a reliable method.The result is that classification using the ANN method gave thearound 83% performance of tapping the RH side is higher thanthe performance of around 73% for the RH tapping as shown inTable 4.

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Thanh Hai et al. Temporal hemodynamic classification using NIRS

FIGURE 10 | Regressed polynomial corresponding to hand tapping. (A)

Regressed polynomial of 8 channels of the left hand tapping. Blue curvesare the signals on the left brain side and red curves are of the right

brain side. (B) Regressed polynomial of 8 channels of right hand tapping.Blue curves are the signals on the left brain side and red curves are ofthe right brain side.

Table 2 | The arrangement of the regressed coefficients of hand tapping tasks for obtaining the input of the recognition networks.

Left hand tapping coefficients Right hand tapping coefficients

Ch-2 Ch-5 Ch-6 Ch-9 Ch-12 Ch-15 Ch-16 Ch-19

h21 . . . h26 h51 . . . h56 h61 . . . h66 h91 . . . h96 h121 . . .126 h151 . . .156 h161 . . .166 h191 . . . h196

All the results of hand tapping tasks obtained here have theaccuracy of more than 70%. In this research, two methods wereapplied to find the best one. The first method is that the SVMalgorithm is used for recognition on three subjects and produce

different performances. In particular, Subject-3 with tapping theRH has the best result with over 80% of the accuracy, while theaccuracy of Subject-2 is only 75% for the case of the RH tappingand 72.5% for that of the LH tapping. While the second method

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Table 3 | Experiment result of 3 subjects with SVM.

Run Hand Accuracy— Accuracy— Accuracy—

tapping subject 1 (%) subject 2 (%) subject 3 (%)

1 Right 70 80 80

Left 80 80 70

2 Right 90 80 80

Left 100 70 90

3 Right 90 60 90

Left 80 70 100

4 Right 70 80 80

Left 70 70 70

Average Right 80 75.0 82.5

Left 82.5 72.5 82.5

Table 4 | Experiment result of 3 subjects with ANN.

Run Hand Accuracy— Accuracy— Accuracy—

tapping subject 1 (%) subject 2 (%) subject 3 (%)

1 Right 80 80 70

Left 70 80 60

2 Right 90 90 80

Left 80 70 80

3 Right 90 70 100

Left 70 70 80

4 Right 80 90 80

Left 70 80 70

Average Right 85 82.5 82.5

Left 72.5 75 72.5

using the ANN is that the accuracy in the case of the RH tap-ping is equal to or greater than 82.5%. In particular, Subject-1 hasthe best accuracy of the right tapping, while the accuracy of theleft tapping just stops at 72.5% for both Subject-1 and Subject-3. Moreover, the result is that Subject-2 has the best accuracy inthe case of the LH tapping. It is clear that two methods used inthis research give a little bit different performance. In general, theSVM method is better in this case. We also observed that Subject-2 produced the best accuracy in the case of the LH tapping. Theright tapping accuracy is greater than the left tapping of all threesubjects in the case of the SVM is performed. Each classificationnetwork has a different response to the same inputs. It can givethe good accuracy in some cases of the right tapping, but it canshow the poor in others. Because of this selective problem, oneshould more carefully choose the classification network type toobtain the higher performance.

In recent years, researchers have proposed different algorithmsin exploring body activities related to human brain. The poorspatial resolution of NIRS made it difficult to distinguish twoclosely located cortical areas from each other. A combination of

the multi-channel NIRS and a Center of Gravity (CoG) approachwidely accepted in the field of Transcranial Magnetic Stimulation(TMS) could be used to discriminate between closely locatedcortical areas activated during hand and foot movements of thesubject (Koenraadt et al., 2012). Hemodynamic responses weremeasured using a NIRS system of 8 channels. For estimating adaptof Oxyhemoglobin (OHb) and Deoxyhemoglobin (HHb), a CoGalgorithm was determined for each condition using the meanhemodynamic responses and the coordinates of the channels.Therefore, significant hemodynamic responses were found forhand and foot movements. This is the interesting methods whichcan be applied to develop for identifying hand tapping. Based onthis information, the proposed algorithms in our research canbe improved with some thresholds to find out which channelgives the valuable information. The order of the filter we hadchosen here belongs to the pulses time of moving hand up anddown. Thus, the method to quantitatively estimate the start andend timing of the hand movement using the neural network wasproposed.

In (Muroga et al., 2006), the authors measured regional cere-bral blood flow during tapping movement of the RH using NIRStechnique. The following tendencies of total-Hb were observed,in which Hb increased within 10 s from the movement start time,decreased within 10 s from the movement end time. The directionof arm force from hemoglobin concentration changes measuredby using NIRS technique was discriminated. A Self-OrganizingMap (SOM) was used to classify the force direction informa-tion obtained from the NIRS signals. The results confirmed thatthe direction of the arm force is discriminable through the NIRSsignal. In the simple classification approach, the average discrim-ination rate gave the performance of 87.5% for two directions.The experimental results showed that the NIRS signal from armforce contained information related to the force directions (Satoet al., 2009). This research is from our research about the pro-posed methods and experimental tasks. While the SOM method,possibly called the ANN, evaluated the arm force directions withthe 87.5% large performance is a little bit higher than that com-pared with the SVM and the ANN for the LH and RH tappingtasks. This is one of methods which we need to apply for ourexperimental tasks to determine the best one.

In using NIRS technology, the local distribution of fingers(right thumb and ring finger, respectively) was distinguished tohemodynamic responses on Somatosensory cortex by the elec-trical stimuli intensity (SI), whose results showed in good accor-dance with the anatomical arrangement of hand area (Xu et al.,2007). Another application is that in NIRS-based brain activa-tion mapping, a novel real-time NIRS signal analysis frameworkbased on the General Linear Model (GLM) and the Kalmanestimator was proposed (Ge et al., 2010). A set of simulateddata was processed using the proposed framework. The resultsobtained suggested that the method can effectively locate brainactivation areas in real-time, thereby demonstrating its poten-tial for real-time NIRS-based brain imaging applications. Boththese researches, the authors were proposed the same experimentwith finger movements using different methods. It is clear thatthe NIRS technology is not only used to distinguish hand tappingtasks in this paper, but also applied for finger movement tasks.

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From the previous researches, we have realized that the pro-posed algorithm can be accompanied with other algorithms forfinding more accuracy. The NIRS technology has been used toobtain Oxy-Hb signals in recent years. However, these Oxy-Hbsignals always exist noises and artifacts due to subject move-ments, noisy environments, human biological changes and oth-ers. Proposing a good method for estimating Oxy-Hb concen-tration changes related to brain activities is always necessary toresearchers. In particular, the poor resolution in spatial domainneeds to be overcome and also applications in real time arean interesting field for research developments using the NIRStechnology.

CONCLUSIONIn this paper, original brain signals of hand tapping tasks werefiltered by the Savitzky–Golay filter to produce the smooth sig-nals. Moreover, the smoothed signals of the LH and RH tappingtasks corresponding to Oxy-Hb concentration changes in humanbrain were analyzed using the PR algorithm. Based on different

coefficients of the curves obtained from the PR algorithm, theANN and SVM algorithms were employed to validate Oxy-Hbdata for the recognition of the hand tapping times. Experimentalresults with hand tapping times showed that one could distin-guish the LH or RH tapping tasks of the subject. In addition, fromthe obtained results of two methods, it was realized that the SVMalgorithm is faster than the ANN one in term of time recognition.Based on the proposed algorithms, future work is that experi-ments will be developed on many subjects to investigate moreaccuracy and to apply for treatment and rehabilitation.

ACKNOWLEDGMENTSWe would like to thank Vietnam National Foundation forScience and Technology Development-NAFOSTED for support-ing research grant No. 106.99-2010.11. Furthermore research waspartly supported by a research fund from the Vietnam NationalUniversity in Ho Chi Minh City. Finally, an honorable mentiongoes to our volunteers, families and friends for their supports onus in completing this project.

REFERENCESAhmed, K. S. (2011). Wheelchair

movement control VIA human eyeblinks. Am. J. Biomed. Eng. 1, 55–58.doi: 10.5923/j.ajbe.20110101.09

Aihara, T., Takeda, Y., Takeda, K.,Yasuda, W., Sato, T., Otaka,Y., et al. (2012). Cortical cur-rent source estimation fromelectroencephalography in com-bination with near-infraredspectroscopy as a hierarchical prior.Neuroimage 59, 4006–4021. doi:10.1016/j.neuroimage.2011.09.087

Aydore, S., Mıhcak, M. K., Ciftci, K.,and Akın, A. (2010). On tempo-ral connectivity of PFC via Gauss–Markov modeling of fNIRS sig-nals. IEEE Trans. Biomed. Eng. 57,761–768. doi: 10.1109/TBME.2009.2020792

Blankertz, B., Dornhege, G., Krauledat,M., Müller, K.-R., Kunzmann, V.,Losch, F., et al. (2006). The Berlinbrain-computer interface:EEG-based communication withoutsubject training. IEEE Trans. NeuralSyst. Rehabil. Eng. 14, 147–152. doi:10.1109/TNSRE.2006.875557

Bozkurt, A., Rosen, A., Rosen, H.,and Onaral, B. (2005). A portablenear infrared spectroscopy systemfor bedside monitoring of newbornbrain. Biomed. Eng. Online 4:29. doi:10.1186/1475-925X-4-29

Brink, T. T., Urton, K., Held, D.,Kirilina, E., Hofmann, M. J., Klann-Delius, G., et al. (2011). The roleof orbitofrontal cortex in processingempathy stories in 4- to 8-year-oldchildren. Front. Psychol. 2:80. doi:10.3389/fpsyg.2011.00080

Cai, X., Chen, X., Yin, S., and Chen,C. (2007). “Wavelet image fusion

based on the high order poly-nomial regression,” in TheIEEE Conference on Geoscienceand Remote Sensing Symposium,(Barcelona), 3100–3103.

Chamasemani, F. F., and Singh, Y. P.(2011). “Multi-class Support VectorMachine (SVM) classifiers–anapplication in hypothyroid detec-tion and classification,” in The 2011Sixth International Conference onBio-Inspired Computing, (Penang),351–356.

Chunguang, L., Tao, L., Yoshio, I.,and Kyoko, S. (2010). “Evaluationof a bimanual-coordinated upper-limbs training system based on thenear infrared spectroscopic signalson brain,” in The 32nd AnnualInternational Conference of the IEEEEMBS, (Buenos Aires), 6625–6628.doi: 10.1109/IEMBS.2010.5627143

Critchley, H. D. (2009).Psychophysiology of neural,cognitive and affective integration:fMRI and autonomic indicants.Int. J. Psychophysiol. 73, 88–94. doi:10.1016/j.ijpsycho.2009.01.012

Cui, X., and Alwan, A. (2005). Noiserobust speech recognition using fea-ture compensation based on poly-nomial regression of utterance SNR.IEEE Trans. Speech Audio Process. 13,1161–1172. doi: 10.1109/TSA.2005.853002

Dake, W., and Chengwei, M. (2006).“The Support Vector Machine(SVM) based near-infrared spec-trum recognition of leaves infectedby the leafminers,” in The ICICIC’06. First International Conferenceon Innovative Computing,Information and Control, (Beijing),448–451.

Ge, S. S., Hu, X.-S., and Hong,K.-S. (2010). “NIRS basedbrain activation mapping:a real-time application,” inInternational Conference on ControlAutomation and Systems (ICCAS),(Gyeonggi-do), 2254–2256.

Gentili, R. J., Ayaz, H., Hadavi, C.,Shewokis, P. A., and Contreras-Vidal, J. L. (2010). “Hemodynamiccorrelates of visuomotor motoradaptation by functional nearinfrared spectroscopy,” in 32ndAnnual International Conference ofthe IEEE EMBS, (Buenos Aires),2918–2921. doi: 10.1109/IEMBS.2010.5626284

Gorry, P. A. (1990). General least-squares smoothing and differentia-tion by the convolution (Savitzky-Golay) method. Anal. Chem. 62,570–573. doi: 10.1021/ac00205a007

Grossmann, T. (2013). The roleof medial prefrontal cortex inearly social cognition. Front.Hum. Neurosci. 7:340. doi:10.3389/fnhum.2013.00340

Hu, X.-S., Hong, K.-S., Ge, S. S.,and Jeong, M.-Y. (2010). Kalmanestimator- and general linearmodel-based on-line brain acti-vation mapping by near-infraredspectroscopy. J. Biomed. Eng. Online9, 1–15. doi: 10.1186/1475-925X-9-82

Ince, N., Goksu, F., Tewfik, A., andArica, S. (2009). Adapting subjectspecific motor imagery EEG pat-terns in space-time-frequency for abrain computer interface. Biomed.Signal Process. Control 4, 236–246.doi: 10.1016/j.bspc.2009.03.005

Kauhanen, L., Nykopp, T., Lehtonen,J., Jylänki, P., Heikkonen, J.,

Rantanen, P., et al. (2006). EEGand MEG brain-computer inter-face for tetraplegic patients.IEEE Trans. Neural Syst.Rehabil. Eng. 14, 190–193. doi:10.1109/TNSRE.2006.875546

Khan, M. A. H., Muntasi, T., andLayek, M. A. (2011). “Multiplepolynomial regression for modelinga MOSFET in saturation to validatethe early voltage,” in The IEEESymposium on Industrial Electronicsand Applications, (Langkawi),261–266. doi: 10.1109/ISIEA.2011.6108712

Khoa, T. Q. D., and Nakagawa, M.(2008). Functional near infraredspectroscope for cognition braintasks by wavelets analysis and neu-ral networks. Int. J. Biol. Life Sci. 4,28–33.

Koenraadt, K. L. M., Duysens, J.,Smeenk, M., and Keijsers, N. L.W. (2012). Multi-channel NIRSof the primary motor cortexto discriminate hand from footactivity. J. Neural Eng. 9, 4. doi:10.1088/1741-2560/9/4/046010

Lloyd-Fox, S., Blasi, A., and Elwellce,C. E. (2010). Illuminating thedeveloping brain: the past, presentand future of functional nearinfrared spectroscopy. Neurosci.Biobehav. 34, 269–284. doi: 10.1016/j.neubiorev.2009.07.008

Macnab, A., Shadgan, B., Afshar, K.,and Stothers, L. (2011). Near-infrared spectroscopy of thebladder: new parameters for eval-uating voiding dysfunction. Int.J. Spectrosc. 2011:814179. doi:10.1155/2011/814179

Molavi, B., and Dumont, G. (2010).“Wavelet based motion artifact

Frontiers in Human Neuroscience www.frontiersin.org September 2013 | Volume 7 | Article 516 | 11

Thanh Hai et al. Temporal hemodynamic classification using NIRS

removal for functional near infraredspectroscopy,” in The 32nd AnnualInternational Conference of the IEEEEMBS, (Buenos Aires), 5–8. doi:10.1109/IEMBS.2010.5626589

Montgomery, D. C., and Runger,G. C. (2003). “Applied statisticsand probability for engineers,” inApplied Statistics and Probability forEngineers, ed W. Anderson (NewYork, NY: John Wiley and Sons Inc),411–418.

Muroga, T., Tsubone, T., and Wada,Y. (2006). “Estimation algo-rithm of tapping movement byNIRS,” in SICE-ICASE, 2006.International Joint Conference,(Busan), 3845–3849. doi: 10.1109/SICE.2006.314804

Ngo, C. Q., Nguyen, T. H., andVo, T. V. (2012). “Linear regres-sion algorithm for hand tappingrecognition using functional nearinfrared spectroscopy,” in TheFourth International Conference onthe Development of BiomedicalEngineering (Ho Chi Minh:Springer).

Orfanidis, S. J. (2010). “Signal process-ing applications,” in Introduction toSignal Processing, ed S. J. Orfanidis(Englewood Cliffs, NJ: PearsonEducation Inc.), 427–453.

Pedrini, L., Magnoni, F., Sensi,L., Pisano, E., Ballestrazzi, M.S., Cirelli, M. R., et al. (2012).Is near-infrared spectroscopya reliablemethod to evalu-ate clamping ischemia duringcarotid surgery? Stroke Res. Treat.2012:156975. doi: 10.1155/2012/156975

Reher, P., Chrcanovic, B. R., Springett,R., and Harris, M. (2011). Nearinfrared spectroscopy: a diagnostictool to evaluate effects of radiother-apy in the mandible? Spectroscopy26, 11–32. doi: 10.3233/SPE-2011-0524

Rifai Chai, G. P. H. (2012). “Mentaltask classifications using prefrontalcortex electroencephalograph sig-nals,” in 34th Annual InternationalConference of the IEEE EMBS, (SanDiego, CA), 1831–1834.

Sato, T., Ito, M., Suto, T., Kameyama,M., Suda, M., Yamagi-Sh, Y., et al.(2007). Time courses of brainactivation and their implicationsfor function: a multichannel near-infrared spectroscopy study duringfinger tapping. Neurosci. Res. 58,297–304. doi: 10.1016/j.neures.2007.03.014

Sato, T., Tsubone, T., and Wada, Y.(2009). “Estimation of the directionof arm force by using NIRS sig-nals,” in Engineering in Medicine andBiology Society, 2009. EMBC 2009,(Minneapolis, MN), 590–593.

Savitzky, A. (1964). Smoothing anddifferentiation of data by sim-plified least squares procedures.Anal. Chem. 36, 1627–1639. doi:10.1021/ac60214a047

Schecklmann, M., Ehlis, A., Plichta, M.,and Fallgatter, A. (2010). Influenceof muscle activity on brain oxygena-tion during verbal fluency assessedwith functional near-infraredspec-troscopy. Neurosci. Biobehav.171, 434–442. doi: 10.1016/j.neuroscience.2010.08.072

Shawe-Taylor, N. C. A. J. (2000).“Support vector machines,” inAn Introduction to Support VectorMachines and Other Kernel-basedLearning Methods (CambridgeUniversity Press), 93–112.

Shimadzu Corporation. (2010).Instruction manual for functionaloptical imaging system FOIRE-3000series. Manual No. M587–E005,117.

Shimokawa, T., Misawa, K. S. T., andMiyagawa, K. (2009). Predictabilityof investment behavior from braininformation measured by func-tional near-infrared spectroscopy:a bayesian neural network model.Neurosci. Res. 161, 347–358. doi:10.1016/j.neuroscience.2009.02.079

Singla, R., Chambayil, B., Khosla, A.,and Santosh, J. (2011). Comparisonof SVM and ANN for classificationof eye events in EEG. J. Biomed.Sci. Eng. 4, 62–69. doi: 10.4236/jbise.2011.41008

Sitaram, R., Zhang, H., Guan, C.,Thulasidas, M., Hoshi, Y., Ishikawa,

A., et al. (2007). Temporalclassification of multichan-nel near-infrared spectroscopysignals of motor imagery fordeveloping a brain–computer inter-face. Neuroimage 34, 1416–1427.doi: 10.1016/j.neuroimage.2006.11.005

Steinier, J., Termonia, Y., and Deltour,J. (1972). Comments on smooth-ing and differentiation of data bysimplified least square procedure.Anal. Chem. 44, 1906–1909. doi:10.1021/ac60319a045

Subasi, A., Kiymik, M. K., Alkan, A.,and Koklukaya, E. (2005). Neuralnetwork classification of EEGsignals by using AR with MLEpreprocessing for epileptic seizuredetection. Math. Comput. Appl. 10,57–70.

Tanaka, K., Matsunaga, K.,and Wang, H. O. (2005).Electroencephalogram-based con-trolof an electric wheelchair. IEEETrans. Rob. 21, 762–766. doi:10.1109/TRO.2004.842350

Taniguchi, H., Hiyamizu, M.,Tominaga, T., and Morioka, S.(2012). Brain activity stimulatedby prismadaptation tasks utilizedfor the treatment of unilateralspatial neglect: a study with fNIRS.Rehabil. Res. Pract. 2012:312781.doi: 10.1155/2012/312781

Tsunashima, H., and Yanagisawa,K. (2009). Measurement ofbrain function of car driverusing Functional Near-InfraredSpectroscopy (fNIRS). Comput.Intell. Neurosci. 2009:164958. doi:10.1155/xya2009/164958

Weiskopf, N., Scharnowski, F., Veit,R., Goebel, R., Birbaumer, N., andMathiak, K. (2004). Self-regulationof local brain activity using real-time functional magnetic resonanceimaging (fMRI). J. Physiol. 98,357–373. doi: 10.1016/j.jphysparis.2005.09.019

Wolpaw, J., Birbaumer, N., McFarland,D., Pfurtschellere, G., and Vaughan,T. (2002). Brain-computer inter-faces for communication andcontrol. Clin. Neurophysiol. 113,

767–791. doi: 10.1016/S1388-2457(02)00057-3

Xu, M., Takata, H., Sheng, Ge., Hayami,T., Yamasaki, T., Tobimatsu, S., et al.(2007). “NIRS measurement ofhemodynamic evoked responsesin the primary somatosensorycortex by finger stimulation,”in IEEE/ICME InternationalConference on Complex MedicalEngineering, 2007. CME 2007,(Beijing), 1425–1429.

Zhang, Z. G., Chan, S. C., Zhang,X., Lam, E. Y., Wu, E. X., andHu, Y. (2009). “High-resolutionreconstruction of human brainMRI image based on local polyno-mial regression,” in Proceedings ofthe 4th International IEEE EMBSConference on Neural Engineering,(Antalya), 245–248.

Conflict of Interest Statement: Theauthors declare that the researchwas conducted in the absence of anycommercial or financial relationshipsthat could be construed as a potentialconflict of interest.

Received: 19 July 2013; paper pend-ing published: 29 July 2013; accepted:12 August 2013; published online: 02September 2013.Citation: Thanh Hai N, Cuong NQ,Dang Khoa TQ and Van Toi V (2013)Temporal hemodynamic classificationof two hands tapping using functionalnear—infrared spectroscopy. Front.Hum. Neurosci. 7:516. doi: 10.3389/fnhum.2013.00516This article was submitted to the journalFrontiers in Human Neuroscience.Copyright © 2013 Thanh Hai, Cuong,Dang Khoa and Van Toi. This is an open-access article distributed under the termsof the Creative Commons AttributionLicense (CC BY). The use, distribution orreproduction in other forums is permit-ted, provided the original author(s) orlicensor are credited and that the origi-nal publication in this journal is cited, inaccordance with accepted academic prac-tice. No use, distribution or reproductionis permitted which does not comply withthese terms.

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