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The Effect of Noise Removing on EmotionalClassification

Maziyar molavi, Jasmy bin YunusFaculty of Health Science and Biomedical Engineering, University Teknologi Malaysia

Johor, Malaysiammaziyar2@live.utm.my, jasmy@fke.utm

 Abstract-This paper explains the issues of study that wasdesigned to evaluate the effect of denoising algorithm to detectemotional expression through Electroencephalogram (EEG).This research led to classify the EEG features due to emotionwhich was induced by the facial expression stimulus include of happy and sad and neutral cases. Event-related potential (ERP)method was selected to probe the ability of Independentcomponents analysis (ICA) and principal components analysis(PCA) as denoising mathematical tool which is used for datapreprocessing. The features were extracted by common spatialpatterns (CSP) to decrease the dimensions of data. After thatextracted components was classified by support vector machine(SVM) to show the effect of noise removing on dataclassification. The results show that ICA could provide themost accurate result for classifying emotional states in brainactivity than other methods. However, the PCA was not showna very different and inaccurate classification results.

I. I NTRODUCTION

When the emotional processes were activated byindividual perceives a motivationally relevant stimulus, thecentral and peripheral nervous show different features of  bioelectrical signal, which is recorded by EEG devises.One of the most useful stimuli to arise the motivation isfacial expression [1]. It caused to induce the variety levelsof emotional feeling such as sadness, happiness, etc. Themost significant question come from the effect of emotionalsates on the body condition, specifically brain function andits dynamic. According to this fact reaction of people whichwere participated in emotional tests [2], their skin reaction[3], cortex, amygdala functional activities [4], and ERPcomponents [5] were evaluated.

Therefore, emotion recognition was probed by a SVMclassify method when the EEG recorded were denoised bydifferent methods. The effect of preprocessing step on processing and classifying levels could show the crucial roleof preprocessing stage mainly constitutes of removal of 

noise, artifacts, and other external interferences.

II. BACKGROUND

Several studies mentioned emotional inducing by facialexpression and tried to explain its dynamic [6,7,8]. Thereare several methods how emotions can be elicited for  research experiment [9,10]. The emotion groups arefundamentally divided into three classes:

1-Motivational group liked Thirst, Hunger, etc.2-Basic group such as Happy, Sad, Fear, Disgust, Anger,

etc.3- Social group liked Shame, Embarrassment, Pride, Guilt,

etc.

 Pattern recognition and classification efforts intended atdiscovery physiological correlates.

It is reported some researchers to recognize the patterns of these emotional categories [11,12]. With the development of technology of electronic devises and computing ability, ERPtechnique has been used to investigation cognition andemotion [13,14]. One of the most important limitations to

use this technique is the amount of noise levels in recordedEEG. Eye blinking and heart beat and muscle activity arethe most common source of noise from inside the body. Toremove the effect of an artifact many of the algorithms werenominated by signal processing researchers [15,16,17].However the ability of denoising algorithms in variety of conditions is in the edge of research until today.

Wavelet technique as an important algorithm to denoisingthe EEG recently applied in many studies successfully [18,19, 20].In other way blind source separation based onindependent component analysis (ICA) could receive specialattention to enhance the signal noise ratio [21,22,23,24].PCA also showed the ability to remove the noise for brainsignals [25,26].

In this paper, these three methods compared to outcomethe better result for classification when SVM performed as aclassification tool. SVM provides a dividing hyper planewith the optimized margin in the high-dimensional space[27]. To solve the problem of the optimal frequency interval,SVM with a different level of signal to noise ratio tested.The expressive features classified in three classes of  emotional components. It was included the happy and sademotional stimulus to induce the different valence andarousal level of emotions. Furthermore, it was conducted toreveal that the emotions aroused with specific dynamics,which were due to independent subjects and stimulus.

III. METHODOLOGY

A. PARTICIPANTSWe studied 12 postgraduate students from Universiti

Teknologi Malaysia. Two participants were rejected, because of depression illness background. The ages of thesubjects were between 25 and 32 years old with a mean ageof 28 years

We evaluated the response of subjects to emotionalstimulus when facial expression screening on the monitoring board. Participating subjects all normal (or corrected) vision,no serious head injury, no epilepsy, and no psychiatricdisease.

978-1-4673-1938-6/12/$31.00 ©2012 IEEE

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B. METHOD The research methodology of this work has been shown

in Fig 1. The brain activity is recorded through the electrodeof EEG machine. Recorded signals are undergone for  preprocessing to remove the noise and artifact. Noise

reduction is done by using Wavelet Transform or ICA or PCA. After the preprocessing, wavelet transform will beapplied for extracting the components from the recorded bioelectrical signal. Normally, the EEG recorded data arenon-stationary; hence the statistical characteristic of the datalike Mean, covariance, Variance, effective Power, Power Spectral Density Function, and other parameters.

After this, the Wavelet transform coefficients aredimensionally reduced for simplifying the classification process and denoising algorithm.

Figure 1. Diagram for Human Emotion Recognition

 Noise reduction can be performed by numerous methodssuch as, Principal Component Analysis (PCA), IndependentComponent Analysis (ICA).

In next step ERP analyze extracted features and Common

Spatial Patterns (CSP) reduced the dimensional of signal.Support vector machine (SVM) classified three differentkinds of emotions sad, Happy, and Neutral.

C. EEG ACQUISITION 3 sets of facial expression pictures selected to induce the

emotional statuses. As a first class happy faces with pleasedand for second group the neutral expressions and in third setthe sad faces screening. The emotional contents of the pictures were calculated by a self-assessment manikin(SAM) including nine gages for the valence and the arousalcontent [28]. It was essential to all students to tag every pictures using SAM when the experiment ended.

All emotional faces and were saved in the database andreloaded arbitrarily by the software. To synchronizationamong the EEG machine and PC which screening theimages, LABVIEW controlled the process. One to twomilliseconds was the time resolution of this synchronizing.Moreover, this software produced an alarm signal to theEEG recorder devices for goal detecting by pressing thespecified keypad. The international 10-20 system wasreference to scalp EEG recording with Ag/AgCl electrodesat 16 electrode sites (F3, Fz, F4, T3, C3, Cz, C4, T4, T5, P3,Pz, P4, T6, O1, Oz, O2) grounded by earlobes connection.The vertical and horizontal electrooculogram (EOG) wasverified at the outer canthi area, under and above the left eye.

The band pass filter was adjusted inside the 0.15-100 Hzwhich is sampled by 500 Hz.

D. PREPROCESSINGFor preprocessing and rejection the noise three

methods were performed. Wavelet, ICA, PCA.1) WAVELET: The WT gives a decomposition of x(t) inThe different scales, tending to maximize at those scales andtime position where the wavelet best resembles x(t). Themultiresolution decomposition divides the signal intodetails at diverse scales, the residual portion being acoarser illustration of the signal called approximation.In this research, eight-level decomposition was used, thushaving eight scales of details (d1d8) and a concludingapproximation (a8). The lower factors give the detailscorresponding to the higher frequency components and theupper levels the ones corresponding to the lower frequencies.Quadratic bi-orthogonal B-Splines were selected as the basic wavelet coefficients due to their similarity with the

event related components (thus having a decent localizationof the ERPs in the wavelet function), and due to their optimal time-frequency resolution.

. The activity of the average ERP is decomposed inDifferent scales and times using the wavelet multiresolutiondecomposition.

. Wavelet coefficients correlated with the ERPs areidentified and the remaining ones are set to zero. The chosencoefficients should cover a time Range in which the single-trial ERPs are expected to occur.

. The inverse transform is applied, thus obtaining adenoised average ERP.

. The denoising scheme defined by the previous Steps is

applied to all single-trials [29].

2) ICA:  Independent Component Analysis (ICA) is analgorithm for noise reduction and artifact problem. It can becharacterized mathematically according to Hyvarinen,Karhunen & Oja [30] as:

X = A s + n (1)Where X is the observed signal, n is the noise factor, A is

the mixing matrix and s the independent components (ICs)or sources. (It can be seen that mathematically it is similar toEq. (1)).

The challenge is to estimate A and recover s knowingonly the measured signal X (equivalent to E(t) in Eq. (1)).This leads to finding the linear transformation W of X. the

inverse of the mixing matrix A, to produce the independentcomponents as:

u = W X = W A s (2)Where u is the estimated ICs. For this solution to work the

assumption is made that the components are statisticallyindependent, while the mixture is not.

By removing the main component of deconstructed signalwhich included the noise and reconstruction of signal theeffect of noise reduced .it is the main idea to perform ICAfor noise cancelation [22].

3) PCA: The PCA a is perfect candid to noise cancellation.First, the reference channels r(t) are time-shifted by a seriesof multiples of the sampling period, both positive and

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negative: r(t+n), n = N/2 +1, , N/2. Second, the set of time-shifted reference signals is orthogonalized by usingPCA, to provide a basis of JN orthogonal time-domaincomponents. Third, all scalp sensor recorded signal is projected onto this basis, and the projection removed. The

result is the clean signal. For brain sensor k the overall process can be indicated as:

(3)

  Where k(t) is the cleaned signal and the [ kj(n)] resultedfrom the reconstruction of orthogonalization and projection.Coefficient kj(n) can be presented as the n-th component of an N-tap finite signal response (FIR) filter applied toreference j before subtraction from EEG signal k. This filter is optimal to minimize the contribution of noise content tothe scalp sensor. It is mentioned that the scalp sensor signals

Sk(t) are not filtered, and thus there is no spectral distortionof EEG [31].

E. FEATURE EXTRACTION  ERP approach was performed to extract featured fromEEG. Time-frequency analysis was applied to detect theeffect of emotional stimuli on brain activity in 5 frequencylevels

Delta frequency (less than 4 Hz)Theta frequency (47 Hz)Alpha frequency (712 Hz)Beta frequency (1230 Hz)

Gamma wave (more than 30 Hz)

By ERP analyze raw power data at each time-frequencylevels sampling changed to log scale due to show normallydistribution and provide appropriate outcome.

F. DATA REDUCTION  Principal Component Analysis (PCA) was used to

reduce the dimensions of the space to reduce the complexityof data and information. EEG signals with the two statuses

1 and 2 are the corresponding approximations of thecovariance matrices.

It is elucidated the two matrices Sd and Sc as follows: Sd = (1) (2) : discriminative activity matrix

Sc = (1)+ (2) : common activity matrixThe CSP Common Spatial Patterns (which C is the

amount of EEG channels) could be achieved by extremizingthe Rayleigh coefficient:

(4)

 To solve this equation we apply an idea like estimatingthe eigenvalue:

Sd   v= v Sc   (5)

The eigenvalue restricted between 1 and 1 value; alarge positive eigenvalue relates to an approximation of thesignal supposed by v that performed with small power in the

second state but large power in the first condition; theopposite of this lemma is truthful for an enormous negativeeigenvalue. The maximum and the minimum eigenvaluescorrespond to the largest and the smallest of the Rayleighcoefficient equation solving (Eq. (5))[32]. In contrast, the

 projection of the signal that is common between two groupsshould be optimized because of avoiding contribute to thediscrimination.

G. CLASSIFICATIONFor classification support vector machine (SVM) is one of 

the most important methods to separate the space by makeoptimum hayperplan. The data points with d-dimensionalhypothesized to be as training arrays. Linear support vector machine performs an improver separating hayperplan withthe maximum margin in this higher-dimensional space.Furthermore, SVM separated the training arrays to two sets.It is aimed to evaluate which categories an experimental setwill belong in.

Training involves the optimization of the error in:

(6)

Subject to the constraints:

(7)

  Where C indicates the constant capacity, w shows thevector of coefficients, b is a constant value and provides parameters for conduct to no separable inputs. The i filedthe N training cases [27].

IV. RESULT

The result of classification performance through differentfrequency bands when the noise was reduced by threeindividual methods are presented and compared in Table I.

 It is proposed that the Alpha, beta and gamma frequencyindicate the higher accuracy output.

TABLE I. THE CLASSIFICATION RESULT BY DIFFERENT DENOISINGMETHODS

Classification

Comparison

Frequency BandDelta Theta Alpha Beta Gama

Denoising

method

wavelet 55.3% 67.2% 81.1% 80.9% 89.3%

ICA 61.4% 75.4% 84.9% 84.1% 91%

PCA 51.1% 69.1% 77.8% 80.1% 88.1%

It designated that higher frequency interval play a moreimportant role in emotional processing than frequency bandsin lower level. Additionally, it is shown the variety effect of denoising method on classification results.in lower frequencies that have a less important role on emotionaldetection the ability of ICA is more than other methods toestablish the higher accurate classes. However in a gamma band which is the more significant frequency interval for detect the emotion the effect of denoising methods onoutputs are very similar.

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It is tried to evaluate the effect of preprocessing level onemotional classification. Three methods include of ICA,PCA and wavelet were performed and compared Theoutcome showed that all of this methods could be used to

denoising of EEG and affected by emotion states. In higher frequency which the brain shows more activation in emotionrecognition SVM provided the better result andapproximately the result was robust in front of the noises.However in lower frequency and similar emotional stimuliICA provided the better result than other methods.According to different level of arousal emotion and varietytypes of characteristics to answer the expressive stimulus, itis suggested that the combine methods of denoisingalgorithms perform to minimize the noise and artifact levelin EEG data.

ACKNOWLEDGMENT

The authors gratefully acknowledge the experimentalassistance from the Faculty of Health Science andBiomedical Engineering, Universiti Teknologi Malaysia.

This work has been supported by international doctoralFellowship (IDF), School of Postgraduate Studies,Universiti Teknologi Malaysia (UTM), Malaysia.

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